CN113015922A - Detection method, detection device and storage medium - Google Patents

Detection method, detection device and storage medium Download PDF

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Publication number
CN113015922A
CN113015922A CN201980068170.4A CN201980068170A CN113015922A CN 113015922 A CN113015922 A CN 113015922A CN 201980068170 A CN201980068170 A CN 201980068170A CN 113015922 A CN113015922 A CN 113015922A
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matrix
target
doppler
noise estimation
determining
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CN113015922B (en
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杨晨
李德建
刘劲楠
劳大鹏
朱金台
周沐
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Huawei Technologies Co Ltd
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Huawei Technologies Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

A detection method, a detection device and a storage medium are used for detecting objects to be tracked, such as over-the-horizon radar, microwave radar, millimeter wave radar, laser radar and the like. The method comprises the following steps: acquiring a frequency spectrum corresponding to a first echo signal received by the radar, and acquiring a range-Doppler matrix according to the first echo signal or a second echo signal received by the radar; and determining one element of the elements of the range-Doppler matrix, wherein the difference value between the element value and one element value in the noise estimation matrix is greater than or equal to a noise threshold, as the object to be tracked. In the detection process, the element values included by the distance-Doppler matrix and the element values included by the noise estimation matrix are directly compared, so that the condition that the object to be tracked is missed to be detected is avoided, and the accuracy and the efficiency of detecting the object to be tracked are improved.

Description

Detection method, detection device and storage medium Technical Field
The present application relates to the field of radar technologies, and in particular, to a detection method, a detection apparatus, and a storage medium.
Background
The prior art provides a millimeter wave radar, which has all-weather environment perception capability and can accurately measure the distance and the speed of an object to be tracked.
In order to obtain the point cloud data of the object to be tracked in the surrounding environment of the millimeter wave radar, a corresponding range-doppler map (R-D map) needs to be obtained according to the received time-frequency signal of the millimeter wave radar. And detecting the object to be tracked on the R-D map by a sliding window Constant False Alarm Rate (CFAR) mode. For example, the processor may detect the R-D map row by row, the processor may determine that the object to be tracked is in the row, the doppler velocity of the object to be tracked is greater than the mean of the doppler velocities of the elements adjacent to the object to be tracked in the row, or, for example, the processor may detect the R-D map column by column, the processor may determine that the object to be tracked is in the column, and the distance of the object to be tracked is greater than the mean of the distances of the elements adjacent to the object to be tracked in the column.
The detection of the object to be tracked is carried out by adopting a CFAR mode, the calculation amount is large, and the condition that the object to be tracked in the surrounding environment of the millimeter wave radar is missed to be detected easily occurs, namely if three objects to be tracked continuously appear in a certain line in an R-D map, the Doppler velocity of the object to be tracked positioned in the middle position is greater than the average value of the Doppler velocities of two adjacent objects to be tracked, and the missed detection of the two adjacent objects to be tracked of the object to be tracked positioned in the middle position is caused. Therefore, the efficiency and the precision of detecting the object to be tracked in a CFAR mode are low.
Disclosure of Invention
The application provides a detection method, a detection device and a storage medium, which can effectively avoid the condition that an object to be tracked is missed to be detected, and further effectively improve the efficiency and the precision of detecting the object to be tracked.
A first aspect of an embodiment of the present invention provides a detection method, configured to detect an object to be tracked, where the method includes: acquiring a frequency spectrum corresponding to a first echo signal received by a radar, wherein a negative frequency axis of the frequency spectrum consists of the frequency of noise and corresponding amplitude; determining a noise estimation matrix according to the negative frequency axis of the frequency spectrum, wherein the noise estimation matrix comprises an element taking the value of the amplitude or an element of the Doppler velocity converted from the amplitude; acquiring a range-Doppler matrix according to the first echo signal or the second echo signal received by the radar; and determining one element of the elements of the range-Doppler matrix, wherein the difference value between the element value and one element value in the noise estimation matrix is greater than or equal to a noise threshold, as the object to be tracked.
In the method of the present aspect, the noise estimation matrix determined from the negative frequency axis of the spectrum includes an element value of the amplitude of the noise or the doppler velocity of the noise. In the process of detecting the object to be tracked in the range-doppler matrix, the sizes of element values of a plurality of elements in the range-doppler matrix do not need to be compared, but the difference between the element value of the object to be tracked and one element value in the noise estimation matrix is only required to be larger than or equal to the noise threshold by directly carrying out the difference between the range-doppler matrix and the noise estimation matrix. In the detection process, the element values included by the distance-Doppler matrix and the element values included by the noise estimation matrix are directly compared, so that the condition that the object to be tracked is missed to be detected is avoided, and the accuracy and the efficiency of detecting the object to be tracked are improved.
Based on the first aspect of the embodiment of the present invention, in an optional implementation manner of the first aspect of the embodiment of the present invention, the obtaining a spectrum corresponding to a first echo signal received by a radar includes: acquiring a plurality of signals, wherein the plurality of signals are formed by converting the first echo signal; oversampling the complex signal to obtain sampled data; a first dimension fast fourier transform, FFT, is performed on the sampled data to obtain the spectrum.
By adopting the implementation mode, the amplitude corresponding to the noise in the first echo signal can be processed to the negative frequency axis of the frequency spectrum through the processing of the first echo signal. Therefore, the noise and the non-noise of the first echo signal are effectively distinguished, and the accuracy of detecting the object to be tracked is effectively improved.
Based on the first aspect of the embodiments of the present invention, in an optional implementation manner of the first aspect of the embodiments of the present invention, the radar has a plurality of receiving antennas, and each of the receiving antennas is configured to receive a plurality of the first echo signals, and the determining a noise estimation matrix according to at least part of negative frequencies of a negative frequency axis of the frequency spectrum includes: acquiring a plurality of initial two-dimensional matrixes, wherein the initial two-dimensional matrixes respectively correspond to the receiving antennas, and the initial two-dimensional matrixes comprise the frequency spectrums of the first echo signals received by the corresponding receiving antennas; determining a target two-dimensional matrix, wherein the target two-dimensional matrix is one of the plurality of initial two-dimensional matrices, or the target two-dimensional matrix is formed by superposing the plurality of initial two-dimensional matrices; and determining the noise estimation matrix according to the target two-dimensional matrix.
By adopting the implementation mode, under the condition that the first-dimension FFT is carried out on the first echo signal to obtain the target two-dimension matrix and the second-dimension FFT is not carried out, the noise estimation matrix can be directly obtained according to the target two-dimension matrix, the number of times of carrying out FFT is effectively reduced, the calculation process is simplified, the efficiency of obtaining the noise estimation matrix is effectively improved, and the efficiency of detecting the object to be tracked is effectively improved.
Based on the first aspect of the embodiments of the present invention, in an optional implementation manner of the first aspect of the embodiments of the present invention, the determining the noise estimation matrix according to the target two-dimensional matrix includes: determining a target distance in the target two-dimensional matrix, wherein the target distance is converted according to the negative frequency included by the frequency spectrum; determining a target amplitude corresponding to the target distance in the target two-dimensional matrix; determining the noise estimation matrix, wherein the noise estimation matrix comprises elements which take the values of the target amplitude.
By adopting the implementation mode, the value of the element included in the noise estimation matrix is the amplitude of the noise, so that the accuracy of detecting the object to be tracked according to the noise estimation matrix is effectively improved.
Based on the first aspect of the embodiment of the present invention, in an optional implementation manner of the first aspect of the embodiment of the present invention, the determining, in the target two-dimensional matrix, a target amplitude corresponding to the target distance includes: and averaging the amplitudes of the target distances respectively corresponding to the target two-dimensional matrix to obtain the target amplitude.
If the target distance respectively corresponds to the amplitudes of F1 and F2 … … FN in the target two-dimensional matrix, the determined target amplitude corresponding to the target distance is F1+ F2 … … FN/N.
Based on the first aspect of the embodiment of the present invention, in an optional implementation manner of the first aspect of the embodiment of the present invention, the determining, in the target two-dimensional matrix, a target amplitude corresponding to the target distance includes: and taking the modulus values of the amplitudes of the target distance respectively corresponding to the target two-dimensional matrix and averaging to obtain the target amplitude.
If the corresponding amplitudes of the target distances in the target two-dimensional matrix are F1 and F2 … … FN, the determined target amplitudes corresponding to the target distances are as follows:
the modulus value of F1+ the modulus value of F2+ the modulus value of FN/N according to the embodiment of the present invention.
Based on the first aspect of the embodiment of the present invention, in an optional implementation manner of the first aspect of the embodiment of the present invention, the determining, in the target two-dimensional matrix, a target amplitude corresponding to the target distance includes: and determining the quantile as the target amplitude in a plurality of amplitudes corresponding to the target two-dimensional matrix according to the target distance. Wherein, the quantile can be a median, a quartile and the like.
By adopting the implementation mode for acquiring the target amplitude, the acquired target amplitude can be closer to the actual amplitude of the noise, so that the accuracy of detecting the object to be tracked according to the noise estimation matrix is effectively improved.
Based on the first aspect of the embodiment of the present invention, in an optional implementation manner of the first aspect of the embodiment of the present invention, after the obtaining a plurality of initial two-dimensional matrices, the method further includes: respectively performing second-dimensional FFT on the initial two-dimensional matrixes to obtain a plurality of initial distance-Doppler matrixes; determining a target range-doppler matrix, wherein the target range-doppler matrix is one of the plurality of initial range-doppler matrices, or the target range-doppler matrix is formed by overlapping the plurality of initial range-doppler matrices; the noise estimation matrix is determined from the target range-doppler matrix.
By adopting the implementation mode, the first-dimension FFT can be carried out on the first echo signal to obtain the two-dimensional matrix, and then the second-dimension FFT is carried out on the two-dimensional matrix to obtain the target distance-Doppler matrix, so that the target to be tracked can be detected more accurately according to the noise estimation matrix obtained by the target distance-Doppler matrix which passes through the two-dimension FFT, and the detection accuracy is improved.
Based on the first aspect of the embodiments of the present invention, in an optional implementation manner of the first aspect of the embodiments of the present invention, the determining the noise estimation matrix according to the target range-doppler matrix includes: determining a target distance in the target distance-Doppler matrix, wherein the target distance is converted according to the negative frequency included in the frequency spectrum; determining a target Doppler velocity corresponding to the target distance in the target distance-Doppler matrix; determining the noise estimation matrix, wherein the noise estimation matrix comprises an element which takes the value of the target Doppler velocity.
By adopting the implementation mode, the value of the element included in the noise estimation matrix is the Doppler velocity of the noise, so that the accuracy of detecting the object to be tracked according to the noise estimation matrix is effectively improved.
Based on the first aspect of the embodiments of the present invention, in an optional implementation manner of the first aspect of the embodiments of the present invention, the determining, in the target distance-doppler matrix, a doppler velocity corresponding to the target distance includes: averaging the Doppler speeds respectively corresponding to the target distance in the target distance-Doppler matrix to obtain the target Doppler speed.
Based on the first aspect of the embodiments of the present invention, in an optional implementation manner of the first aspect of the embodiments of the present invention, the determining, in the target distance-doppler matrix, a doppler velocity corresponding to the target distance includes: obtaining the module values of the Doppler speeds respectively corresponding to the target distance in the target distance-Doppler matrix, and then averaging to obtain the target Doppler speed;
based on the first aspect of the embodiments of the present invention, in an optional implementation manner of the first aspect of the embodiments of the present invention, the determining, in the target distance-doppler matrix, a doppler velocity corresponding to the target distance includes: and determining the quantile as the target Doppler velocity from a plurality of Doppler velocities corresponding to the target distance-Doppler matrix.
By adopting the implementation mode for acquiring the target Doppler velocity, the acquired target Doppler velocity can be closer to the actual Doppler velocity of the noise, so that the accuracy of detecting the object to be tracked according to the noise estimation matrix is effectively improved.
Based on the first aspect of the embodiment of the present invention, in an optional implementation manner of the first aspect of the embodiment of the present invention, before determining the object to be tracked, the method further includes: and when the dimension of the noise estimation matrix is not equal to the dimension of the range-Doppler matrix, processing the dimension of the noise estimation matrix, wherein the processed dimension of the noise estimation matrix is equal to the dimension of the range-Doppler matrix.
By adopting the realization mode, under the condition that the dimension of the noise estimation matrix is not equal to that of the range-Doppler matrix, the dimension of the noise estimation matrix is equal to that of the range-Doppler matrix through a processing mode of reducing or expanding the dimension of the noise estimation matrix, so that the efficiency of making the difference between the noise estimation matrix and the range-Doppler matrix is effectively improved, and the efficiency of detecting the object to be tracked is further improved.
Based on the first aspect of the embodiment of the present invention, in an optional implementation manner of the first aspect of the embodiment of the present invention, before the obtaining a spectrum corresponding to a first echo signal received by a radar, the method further includes: acquiring a signal to be detected from the radar, wherein the signal to be detected is a signal received by the radar from the surrounding environment; acquiring a plurality of signals to be detected, wherein the plurality of signals to be detected are formed by converting the signals to be detected; oversampling the data signal to be detected to obtain sampling data to be detected; performing first-dimension FFT on the sampling data to be detected to obtain a frequency spectrum to be detected; and determining that the amplitude corresponding to any negative frequency included in the negative frequency axis of the frequency spectrum to be detected is less than or equal to the interference threshold.
By adopting the implementation mode, whether an interference signal causing interference to the radar exists can be detected according to the frequency to be detected corresponding to the signal to be detected, and if the amplitude corresponding to any negative frequency included in the negative frequency axis of the frequency spectrum to be detected is determined to be less than or equal to the interference threshold, the interference signal causing interference to the radar is determined not to exist currently. Only under the condition of no interference signal, the object to be tracked is detected, so that the accuracy of detecting the object to be tracked is effectively improved.
Based on the first aspect of the embodiment of the present invention, in an optional implementation manner of the first aspect of the embodiment of the present invention, a first processing cycle and a second processing cycle are preset. The processing timing of the first processing cycle is earlier than the processing timing of the second processing cycle in processing timing. The noise estimation matrix is generated based on a first echo signal received by the radar during a first processing period, and the range-doppler matrix is generated based on a second echo signal received by the radar during a second processing period. The first processing period and the second processing period may be in a one-to-one correspondence relationship, that is, a noise estimation matrix obtained in the first processing period is used to perform noise estimation on the time-frequency signal obtained by the detection apparatus in the second processing period. For another example, the first processing period and the second processing period may be in a one-to-many correspondence relationship, that is, the noise estimation matrix obtained in the first processing period is used to perform noise estimation on the time-frequency signals obtained by the detection apparatus in the subsequent second processing periods, respectively.
Based on the first aspect of the embodiment of the present invention, in an optional implementation manner of the first aspect of the embodiment of the present invention, when the noise estimation matrix is a column matrix, and when the number of first elements included in the noise estimation matrix and the number of second elements included in any column of the range-doppler matrix are equal, a difference is made between each column element included in the range-doppler matrix and the noise estimation matrix. And if the number of the first elements included in the noise estimation matrix is equal to the number of the second elements included in any column of the range-doppler matrix, subtracting the noise estimation matrix from the column-by-column elements included in the range-doppler matrix to obtain a target difference value.
Based on the first aspect of the embodiment of the present invention, in an optional implementation manner of the first aspect of the embodiment of the present invention, when the noise estimation matrix is a column matrix, and when a first element included in the noise estimation matrix and a second element included in any column of the range-doppler matrix are equal in number, the range-doppler matrix is a two-dimensional matrix, and the noise estimation matrix is a one-dimensional matrix. The noise estimation matrix is replicated such that the matrix dimension of the replicated noise estimation matrix is the same as the matrix dimension of the range-doppler matrix. Specifically, the fact that the matrix dimension of the copied noise estimation matrix is the same as the matrix dimension of the range-doppler matrix means that the number of rows included in the copied noise estimation matrix is equal to the number of rows included in the range-doppler matrix, and the number of columns included in the copied noise estimation matrix is equal to the number of columns included in the range-doppler matrix.
Based on the first aspect of the embodiment of the present invention, in an optional implementation manner of the first aspect of the embodiment of the present invention, if the number of first elements included in the noise estimation matrix is greater than the number of second elements included in any column of the range-doppler matrix, the noise estimation matrix is reduced to make the number of elements included in the noise estimation matrix equal to the number of elements included in any column of the range-doppler matrix. If the number of the first elements included in the noise estimation matrix is smaller than the number of the elements included in any row of the range-doppler matrix, the noise estimation matrix is expanded so that the number of the first elements included in the noise estimation matrix is equal to the number of the second elements included in any row of the range-doppler matrix.
A second aspect of the embodiments of the present invention provides a detection apparatus, configured to detect an object to be tracked, including: an acquisition unit, configured to acquire a frequency spectrum corresponding to a first echo signal received by a radar, a negative frequency axis of the frequency spectrum being composed of a frequency of noise and a corresponding amplitude; a processing unit, configured to determine a noise estimation matrix according to a negative frequency axis of the frequency spectrum, where the noise estimation matrix includes an element taking the value of the amplitude or an element of a doppler velocity converted from the amplitude; acquiring a range-Doppler matrix according to the first echo signal or the second echo signal received by the radar; and determining one element of the elements of the range-Doppler matrix, wherein the difference value between the element value and one element value in the noise estimation matrix is greater than or equal to a noise threshold, as the object to be tracked.
The detection apparatus shown in this aspect executes the detection method shown in the first aspect, and please refer to the above description for details, which are not repeated.
Based on the second aspect of the embodiment of the present invention, in an optional implementation manner of the second aspect of the embodiment of the present invention, the obtaining unit is specifically configured to: acquiring a plurality of signals, wherein the plurality of signals are formed by converting the first echo signal; oversampling the complex signal to obtain sampled data; a first dimension fast fourier transform, FFT, is performed on the sampled data to obtain the spectrum.
Based on the second aspect of the embodiment of the present invention, in an optional implementation manner of the second aspect of the embodiment of the present invention, the radar has a plurality of receiving antennas, each of the receiving antennas is configured to receive a plurality of the first echo signals, and the processing unit, in the process of determining the noise estimation matrix according to at least part of negative frequencies of the negative frequency axis of the frequency spectrum, is specifically configured to: acquiring a plurality of initial two-dimensional matrixes, wherein the initial two-dimensional matrixes respectively correspond to the receiving antennas, and the initial two-dimensional matrixes comprise the frequency spectrums of the first echo signals received by the corresponding receiving antennas; determining a target two-dimensional matrix, wherein the target two-dimensional matrix is one of the plurality of initial two-dimensional matrices, or the target two-dimensional matrix is formed by superposing the plurality of initial two-dimensional matrices; and determining the noise estimation matrix according to the target two-dimensional matrix.
Based on the second aspect of the embodiment of the present invention, in an optional implementation manner of the second aspect of the embodiment of the present invention, in the process of determining the noise estimation matrix according to the target two-dimensional matrix, the processing unit is specifically configured to: determining a target distance in the target two-dimensional matrix, wherein the target distance is converted according to the negative frequency included by the frequency spectrum; determining a target amplitude corresponding to the target distance in the target two-dimensional matrix; determining the noise estimation matrix, wherein the noise estimation matrix comprises elements which take the values of the target amplitude.
Based on the second aspect of the embodiment of the present invention, in an optional implementation manner of the second aspect of the embodiment of the present invention, in the process of determining the target amplitude corresponding to the target distance in the target two-dimensional matrix, the processing unit is specifically configured to: and averaging the amplitudes of the target distances respectively corresponding to the target two-dimensional matrix to obtain the target amplitude.
Based on the second aspect of the embodiment of the present invention, in an optional implementation manner of the second aspect of the embodiment of the present invention, the processing unit is further configured to: respectively performing second-dimensional FFT on the initial two-dimensional matrixes to obtain a plurality of initial distance-Doppler matrixes; determining a target range-doppler matrix, wherein the target range-doppler matrix is one of the plurality of initial range-doppler matrices, or the target range-doppler matrix is formed by overlapping the plurality of initial range-doppler matrices; the noise estimation matrix is determined from the target range-doppler matrix.
Based on the second aspect of the embodiment of the present invention, in an optional implementation manner of the second aspect of the embodiment of the present invention, in the process that the processing unit determines the noise estimation matrix according to the target range-doppler matrix, the processing unit is specifically configured to: determining a target distance in the target distance-Doppler matrix, wherein the target distance is converted according to the negative frequency included in the frequency spectrum; determining a target Doppler velocity corresponding to the target distance in the target distance-Doppler matrix; determining the noise estimation matrix, wherein the noise estimation matrix comprises an element which takes the value of the target Doppler velocity.
Based on the second aspect of the embodiment of the present invention, in an optional implementation manner of the second aspect of the embodiment of the present invention, in the process of determining the doppler velocity corresponding to the target distance in the target distance-doppler matrix, the processing unit is specifically configured to: averaging the Doppler speeds respectively corresponding to the target distance in the target distance-Doppler matrix to obtain the target Doppler speed.
Based on the second aspect of the embodiment of the present invention, in an optional implementation manner of the second aspect of the embodiment of the present invention, the processing unit is further configured to: and when the dimension of the noise estimation matrix is not equal to the dimension of the range-Doppler matrix, processing the dimension of the noise estimation matrix, wherein the processed dimension of the noise estimation matrix is equal to the dimension of the range-Doppler matrix.
Based on the second aspect of the embodiment of the present invention, in an optional implementation manner of the second aspect of the embodiment of the present invention, the obtaining unit is further configured to: acquiring a plurality of signals to be detected, wherein the plurality of signals to be detected are formed by converting the signals to be detected; oversampling the data signal to be detected to obtain sampling data to be detected; performing first-dimension FFT on the sampling data to be detected to obtain a frequency spectrum to be detected; and determining that the amplitude corresponding to any negative frequency included in the negative frequency axis of the frequency spectrum to be detected is less than or equal to the interference threshold.
A third aspect of the embodiments of the present invention provides an electronic device, configured to detect an object to be tracked, including a transceiver, a memory and a processor, where the memory is configured to store a computer program, and the processor is configured to read and execute the computer program stored in the memory to perform the detection method according to any one of the first aspect of the embodiments of the present invention.
A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed on a computer, the computer is caused to execute the detection method according to any one of the first aspect of the embodiments of the present invention.
A fifth aspect of the embodiments of the present invention provides a chip, including a processor. The processor is configured to read and execute the computer program stored in the memory to perform the detection method according to any one of the first aspect of the embodiments of the present invention.
Optionally, the chip further comprises a memory, and the memory and the processor are connected with the memory through a circuit or a wire.
A sixth aspect of embodiments of the present invention provides a computer program product comprising computer program code which, when run on a computer, causes the computer to perform the detection method as set forth in any one of the first aspects of embodiments of the present invention described above.
A seventh aspect of the embodiments of the present invention provides a communication system, including an electronic device and a radar, where the electronic device is configured to perform the detection method shown in any one of the first aspect of the embodiments of the present invention.
Drawings
FIG. 1 is a functional schematic block diagram of a millimeter wave radar-equipped vehicle provided herein;
FIG. 2 is an exemplary diagram of a two-dimensional matrix provided by a prior art scheme;
FIG. 3 is a flow chart illustrating steps of an exemplary detection method provided herein;
FIG. 4a is a waveform diagram of an embodiment provided herein;
FIG. 4b is a waveform diagram of another embodiment provided herein;
FIG. 5 is a diagram illustrating an embodiment of a two-dimensional matrix provided herein;
FIG. 6a is a waveform diagram of an embodiment provided herein;
FIG. 6b is a waveform diagram of another embodiment provided herein;
FIG. 7 is a diagram illustrating an embodiment of a noise estimation matrix provided herein;
FIG. 8 is a flow chart illustrating steps of one embodiment of a detection method provided herein;
FIG. 9 is a flowchart illustrating steps of an exemplary detection method according to the present disclosure;
FIG. 10 is a diagram illustrating an exemplary structure of a detecting device provided in the present application;
fig. 11 is a diagram illustrating a structure of an electronic device according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The detection method provided by the application is applied to the radar, and the specific types of the radar are not limited in the application, for example, beyond-the-horizon radar, microwave radar, millimeter wave radar, laser radar and the like.
The millimeter wave radar is used for scanning the surrounding environment by emitting detection signals to obtain echo signals reflected by objects in the surrounding environment, the millimeter wave radar processes the received echo signals to obtain time-frequency signals, the processor detects objects to be tracked in the surrounding environment of the millimeter wave radar based on the time-frequency signals, and the millimeter wave radar can track the detected objects to be tracked. The object to be tracked refers to a person or an object which is located in the surrounding environment of the millimeter wave radar and reflects an echo signal according to a detection signal. The processor may be integrated in the millimeter wave radar, or the processor may be located in a computer device with processing functionality connected to the millimeter wave radar.
The millimeter wave radar has a wide application in the fields of national defense, automatic driving, geographical mapping and the like, and the following description is given by taking the application of the millimeter wave radar provided by the present application to the field of automatic driving as an example with reference to fig. 1, and it is to be understood that the description of the application field of the millimeter wave radar in the present embodiment is an optional example and is not limited specifically.
As shown in fig. 1, fig. 1 is a functional block diagram of a vehicle 100 with an automatic driving function according to an embodiment of the present application. In one embodiment, the vehicle 100 is configured in a fully or partially autonomous driving mode. For example, the vehicle 100 may control the vehicle 100 itself while in the autonomous driving mode, and the current state of the vehicle and its surroundings may be determined by human operation, the possible behavior of at least one other vehicle in the surroundings may be determined, and a confidence level corresponding to the likelihood of the other vehicle performing the possible behavior may be determined, the vehicle 100 being controlled based on the determined information. While the vehicle 100 is in the autonomous driving mode, the vehicle 100 may be placed into operation without human interaction.
The vehicle 100 may include various subsystems such as a travel system 102, a sensor system 104, a control system 106, one or more peripherals 108, as well as a power supply 110, a computer system 122, and a user interface 116. Alternatively, vehicle 100 may include more or fewer subsystems, and each subsystem may include multiple elements. In addition, each of the sub-systems and elements of the vehicle 100 may be interconnected by wire or wirelessly.
The travel system 102 may include components that provide powered motion to the vehicle 100. In one embodiment, the travel system 102 may include an engine 118, an energy source 119, a transmission 120, and wheels/tires 121.
The sensor system 104 may include several sensors that sense environmental information about the surroundings of the vehicle 100. For example, the sensor system 104 may include a positioning system 129 (which may be a Global Positioning System (GPS) system, a Beidou system, or other positioning system), an Inertial Measurement Unit (IMU) 124, a radar 126, a laser rangefinder 128, and a camera 130. Sensor data from one or more of these sensors may be used to detect the object and its corresponding characteristics (position, shape, orientation, velocity, etc.). Such detection and identification is a critical function of the safe operation of the autonomous vehicle 100.
The radar 126 may utilize the probe signals to sense objects to be tracked within the surrounding environment of the vehicle 100. In this embodiment, the radar 126 is a millimeter-wave radar, wherein the millimeter-wave radar has an all-weather environment sensing capability in all days, the detection distance of the millimeter-wave radar is generally 150 meters to 250 meters, and the detection distance of some high-performance millimeter-wave radars can even reach 300 meters, so that the requirement of detecting a large range of vehicles in high-speed motion can be met. Meanwhile, the detection precision of the millimeter wave radar is high, the distance and the speed of the object to be tracked can be accurately measured, and therefore differentiation competitiveness which other vehicle-mounted sensors do not have is provided. The present embodiment does not limit the specific type of the millimeter wave radar, and the present embodiment exemplifies the millimeter wave radar as a Frequency Modulated Continuous Wave (FMCW).
The laser rangefinder 128 may utilize laser light to sense objects to be tracked in the environment in which the vehicle 100 is located. In some embodiments, the laser rangefinder 128 may include one or more laser sources, laser scanners, and one or more detectors, among other system components.
The control system 106 is for controlling the operation of the vehicle 100 and its components. Control system 106 may include various elements including a steering system 132, a throttle 134, a braking unit 136, a sensor fusion algorithm 138, a computer vision system 140, a route control system 142, and an obstacle avoidance system 144.
Of course, in one example, the control system 106 may additionally or alternatively include components other than those shown and described. Or may reduce some of the components shown above.
Vehicle 100 interacts with external sensors, other vehicles, other computer systems, or users through peripherals 108. The peripheral devices 108 may include a wireless communication system 146, an in-vehicle computer 148, a microphone 150, and/or speakers 152.
In some embodiments, the peripheral devices 108 provide a means for the vehicle 100 to interact with the user interface 116. The power supply 110 may provide power to various components of the vehicle 100. Some or all of the functionality of the vehicle 100 is controlled by the computer system 122. The computer system 122 may include at least one processor 113, the processor 113 executing instructions 115 stored in a non-transitory computer readable medium, such as the memory 114. The computer system 122 may also be a plurality of computing devices that control individual components or subsystems of the vehicle 100 in a distributed manner.
The processor 113 may be any conventional processor, such as a commercially available Central Processing Unit (CPU). Alternatively, the processor may be a dedicated device such as an Application Specific Integrated Circuit (ASIC) or other hardware-based processor. Although fig. 1 functionally illustrates a processor, memory, and other elements of the computer system 122 in the same block, those skilled in the art will appreciate that the processor, computer, or memory may actually comprise multiple processors, computers, or memories that may or may not be stored within the same physical housing. For example, the memory may be a hard drive or other storage medium located in a different enclosure than computer system 122. Thus, references to a processor or computer are to be understood as including references to a collection of processors or computers or memories which may or may not operate in parallel. Rather than using a single processor to perform the steps described herein, some components, such as the steering component and the retarding component, may each have their own processor that performs only computations related to the component-specific functions.
In various aspects described herein, the processor may be located remotely from the vehicle and in wireless communication with the vehicle. In other aspects, some of the processes described herein are executed on a processor disposed within the vehicle and others are executed by a remote processor, including taking the steps necessary to perform a single maneuver.
In some embodiments, the memory 114 may contain instructions 115 (e.g., program logic), and the instructions 115 may be executable by the processor 113 to perform various functions of the vehicle 100, such as performing the functions of the detection methods shown herein. The memory 114 may also contain additional instructions, including instructions to send data to, receive data from, interact with, and/or control one or more of the travel system 102, the sensor system 104, the control system 106, and the peripheral devices 108.
In addition to instructions 115, memory 114 may also store data such as road maps, route information, the location, direction, speed of the vehicle, and other such vehicle data, among other information. Such information may be used by the vehicle 100 and the computer system 122 during operation of the vehicle 100 in autonomous, semi-autonomous, and/or manual modes.
A user interface 116 for providing information to and receiving information from a user of the vehicle 100. Optionally, the user interface 116 may include one or more input/output devices within the collection of peripheral devices 108, such as a wireless communication system 146, an on-board vehicle computer 148, a microphone 150, and a speaker 152.
The computer system 122 may control the functions of the vehicle 100 based on inputs received from various subsystems (e.g., the travel system 102, the sensor system 104, and the control system 106) and from the user interface 116. For example, the computer system 122 may utilize input from the control system 106 in order to control the steering unit 132 to avoid obstacles detected by the sensor system 104 and the obstacle avoidance system 144. In some embodiments, the computer system 122 is operable to provide control over many aspects of the vehicle 100 and its subsystems.
Alternatively, one or more of these components described above may be mounted or associated separately from the vehicle 100. For example, the memory 114 may exist partially or completely separate from the vehicle 100. The above components may be communicatively coupled together in a wired and/or wireless manner.
Optionally, the above components are only an example, in an actual application, components in the above modules may be added or deleted according to an actual need, and fig. 1 should not be construed as limiting the embodiment of the present application.
An autonomous automobile traveling on a road, such as vehicle 100 above, may identify objects to be tracked within its surrounding environment to determine an adjustment to the current speed of the vehicle. The object to be tracked may be another vehicle, a traffic control device, or a pedestrian, etc. In some examples, each identified object to be tracked may be considered independently, and based on the respective characteristics of the object to be tracked, such as its current speed, acceleration, separation from the vehicle, etc., may be used to determine the speed at which the autonomous vehicle is to be adjusted.
Optionally, the autonomous automobile vehicle 100 or a computing device associated with the autonomous vehicle 100 (e.g., computer system 122, computer vision system 140, memory 114 of fig. 1) may predict behavior of the identified object to be tracked based on characteristics of the identified object to be tracked and the state of the surrounding environment (e.g., traffic, rain, ice on the road, etc.). Optionally, each of the identified objects to be tracked is dependent on the behavior of each other, so it is also possible to predict the behavior of a single identified object to be tracked considering all the identified objects to be tracked together. The vehicle 100 is able to adjust its speed based on the predicted behavior of the identified object to be tracked. In other words, the autonomous automobile is able to determine what steady state the vehicle will need to adjust to (e.g., accelerate, decelerate, or stop) based on the predicted behavior of the object to be tracked. In this process, other factors may also be considered to determine the speed of the vehicle 100, such as the lateral position of the vehicle 100 in the road on which it is traveling, the curvature of the road, the proximity of static and dynamic objects to be tracked, and so forth.
In addition to providing instructions to adjust the speed of the autonomous vehicle, the computing device may also provide instructions to modify the steering angle of the vehicle 100 to cause the autonomous vehicle to follow a given trajectory and/or to maintain a safe lateral and longitudinal distance from an object to be tracked in the vicinity of the autonomous vehicle (e.g., a car in an adjacent lane on a road).
The vehicle 100 may be a car, a truck, a motorcycle, a bus, a boat, an airplane, a helicopter, a lawn mower, an amusement car, a playground vehicle, construction equipment, a trolley, a golf cart, a train, a trolley, etc., and the embodiment of the present invention is not particularly limited.
The following describes the detection of defects of an object to be tracked in the surrounding environment by the millimeter wave radar:
in order to realize the detection of the object to be tracked, the millimeter wave radar is required to transmit a detection signal to the surrounding environment and receive an echo signal reflected by the object to be tracked in the surrounding environment. After receiving the echo signal, the millimeter wave radar performs front-end processing on the echo signal to obtain a time-frequency signal, where the front-end processing includes filtering, amplification, analog-to-digital conversion (AD), and the like. The processor can acquire the R-D map according to the time-frequency signal, the processor detects the object to be tracked in the R-D map, and the processor can track the object to be tracked based on the detected object to be tracked, for example, if the object to be tracked is a pedestrian or other vehicles in the surrounding environment, the processor can track the person or the vehicle in automatic driving.
To better understand the detection process of the object to be tracked, the following is an exemplary illustration of the R-D map:
the millimeter wave radar generally employs multiple antennas to achieve multiple transmission and multiple reception, so as to improve the resolution of the millimeter wave radar, and specifically, the millimeter wave radar is provided with a transmitting antenna for transmitting a detection signal to the surrounding environment and a receiving antenna for receiving an echo signal reflected by the surrounding environment. More specifically, any transmitting antenna deployed in the millimeter wave radar transmits a probe signal to the surrounding environment in units of frames, and the millimeter wave radar receives a plurality of echo signals through a receiving antenna.
The processor can acquire a plurality of time-frequency signals output by the millimeter wave radar after the millimeter wave radar respectively performs front-end processing on the plurality of echo signals, and the plurality of time-frequency signals are uniform and are at equal intervals. And the processor samples each time-frequency signal in the plurality of time-frequency signals according to a preset sampling point. The specific size of the sampling rate for sampling each time-frequency signal is not limited in this example. For example, if the processor determines that the number of the preset sampling points is 125, the processor samples the time-frequency signal and then obtains 125 sampling points to perform a first Fast Fourier Transform (FFT) to obtain a distance corresponding to a signal frequency acquired by each sampling point. The distance corresponding to each sampling point is the physical distance between an object for reflecting the signal corresponding to each sampling point and the millimeter wave radar. The processor stores the distance corresponding to each sampling point in a two-dimensional matrix in a column form, and the column comprises 125 elements which are sequentially ordered from small to large according to the distance. It should be clear that, here, the example is given by taking the example that the processor stores the distances corresponding to the sampling points in the two-dimensional matrix in the form of columns, and in other examples, the processor may also store the distances corresponding to the sampling points in the two-dimensional matrix in the form of rows.
For better understanding, please refer to fig. 2, where fig. 2 is an exemplary diagram of a two-dimensional matrix, in the two-dimensional matrix shown in fig. 2, a processor acquires 8 time-frequency signals, samples each time-frequency signal, and performs a first-dimension FFT, so that the two-dimensional matrix shown in fig. 2 includes 8 columns of objects, and distances of elements included in each column are sequentially increased in a direction indicated by an arrow 220, and a unit of the distance in this example may be meter (m).
After the processor sets all the received time-frequency signals in the two-dimensional matrix shown in fig. 2, the processor may perform a second-dimensional FFT on all the elements included in each row in the two-dimensional matrix to obtain a doppler velocity of each element in the row, where the unit of the doppler velocity may be meter per second (m/s). The doppler velocity of each element included in each row increases in the direction indicated by the arrow 221.
After the processor performs the two-dimensional FFT shown above, the processor may determine that the two-dimensional matrix shown in fig. 2 is an R-D map, and the following exemplary process of how the processor detects an object to be tracked on the R-D map based on the R-D map shown in fig. 2 is described as follows:
as shown in fig. 2, the processor implements detection of an object to be tracked in an R-D map by using a detection method of a sliding window CFAR based on a cell average-constant false alarm rate (CA-CFAR) or an order statistical-constant false alarm rate (OS-CFAR), and the like, and the specific process is as follows:
the processor firstly performs Doppler sliding window detection line by line according to the direction of the R-D map line, taking the 5 th line of the R-D map, namely the line 201 as an example, the condition met by the object to be tracked 202 which performs Doppler sliding window detection on the line 201 is that the Doppler velocity of the object to be tracked 202 is greater than the average value of the Doppler velocities of the element 203 and the element 204 adjacent to the object to be tracked 202 in the line 201.
After the processor performs detection of the object to be tracked on each row of the R-D map, the processor performs distance sliding window detection on all columns included in the R-D map column by column, taking the column 205 of the R-D map as an example, the condition that the object to be tracked 206, which performs distance sliding window detection on the column 205, satisfies that the distance of the object to be tracked 206 is greater than the average value of the distances of the elements 207 and 208 adjacent to the object to be tracked 206 in the column 205.
The processor can intersect the objects to be tracked detected line by line and the objects to be tracked detected column by column, namely, the processor determines that the objects to be tracked are detected by the Doppler sliding window and the distance sliding window for tracking. Or, the object to be tracked detected by the doppler sliding window performed row by row and the object to be tracked detected by the distance sliding window performed column by column may be merged, that is, the processor determines to track the object to be tracked detected by any dimension sliding window. There are many specific implementations of sliding window CFAR detection mentioned here, for example. And different implementation forms are adopted, and the performance and the operation complexity are different. After the processor detects the object to be tracked, the distance and the Doppler velocity corresponding to the object to be tracked can be determined based on the R-D map, and then the object to be tracked is tracked.
The following describes a detection method based on a sliding window CFAR, which realizes the detection of the defects of an object to be tracked on an R-D map:
by adopting the sliding window CFAR detection mode, the calculation amount is larger, especially as mentioned above, by adopting the implementation form of OS-CFAR, the calculation amount is larger, thereby reducing the efficiency of detecting the object to be tracked. Moreover, no matter what kind of implementation form of CFAR has certain inherent defects, for example, a sliding window CFAR detection mode is adopted, which is very easy to cause missed detection of the object to be tracked, thereby reducing the accuracy of detection of the object to be tracked.
Continuing with fig. 2, the sliding window CFAR-based detection method is very likely to cause a situation where a strong object to be tracked masks a weak object to be tracked. Specifically, the case that the strong object to be tracked covers the weak object to be tracked means that, in the R-D map, the object to be tracked having a larger distance and/or doppler velocity covers the object to be tracked having a smaller distance and/or doppler velocity, so that the processor cannot detect the object to be tracked having a smaller distance and/or doppler velocity, which may cause the missed detection of the object to be tracked. As shown in fig. 2, if the doppler velocity of the object 211 to be tracked is high, the doppler velocity of the object 209 to be tracked is low, and the doppler velocity of the object 209 to be tracked is lower than the average value of the doppler velocities of the adjacent object 211 to be tracked and the element 210 to be tracked, the object 211 to be tracked with high doppler velocity covers the object 209 to be tracked with low doppler velocity due to the existence of the object 211 to be tracked with high doppler velocity, and the processor cannot successfully detect the object 209 to be tracked. Therefore, the accuracy of detecting the object to be tracked is reduced based on the sliding window CFAR detection mode, and the detection omission of the object to be tracked is easily caused when a plurality of objects to be tracked are adjacent by the sliding window CFAR-based detection method.
In order to improve the efficiency and accuracy of detecting an object to be tracked, the present application provides a detection method, and a specific implementation procedure of the detection method provided by the present application is exemplarily described below with reference to fig. 3, where fig. 3 is a flowchart of steps of an embodiment of the detection method provided by the present application.
Step 301, the radar acquires a first echo signal.
In order to improve the accuracy and efficiency of tracking an object to be tracked, the radar shown in this embodiment is configured with a plurality of receiving antennas, and each receiving antenna is configured to receive a plurality of first echo signals. For example, the radar is configured with N receive antennas, N being a positive integer greater than 1. The radar may receive a plurality of first echo signals based on any of the receive antennas.
Step 302, the radar sends the time frequency signal to the detection device.
The detection device shown in this embodiment is used for realizing tracking of an object to be tracked, the specific type of the detection device is not limited in this embodiment, the detection device may be integrated in a radar, or may be separately provided with the radar, and the detection device and the radar are separately provided as examples in this embodiment.
The radar can respectively process the acquired first echo signals through the front end to generate a plurality of time-frequency signals, and send the time-frequency signals to the detection device. For details of the front-end processing process, please refer to the above description, and details are not described here.
Step 303, the detecting device converts the time frequency signal into a complex signal.
Specifically, the detection device converts a plurality of first echo signals received by each receiving antenna of the radar into complex signals respectively. It should be clear that, in the embodiment, the example that the detection device acquires the corresponding complex signal according to the time-frequency signal is taken as an example for illustration, in other examples, the radar may also generate the corresponding complex signal according to the received first echo signal, and then send the complex signal to the detection device.
The following describes how the detection apparatus obtains the corresponding complex signal according to the time-frequency signal:
after the detection device acquires the time-frequency signal, the detection device can equally divide the time-frequency signal into two paths of signals, namely a first signal and a second signal. The detection device phase shifts the first signal by 90 degrees, and determines a complex signal according to the first signal and the second signal, wherein the real part of the complex signal is the second signal, and the imaginary part of the complex signal is the 90-degree shifted first signal.
Step 304, the detecting device performs over-sampling on each complex signal to obtain sampling data.
In this embodiment, when the detection device converts the M first echo signals received by the receiving antenna i into M complex signals, the detection device may perform sampling on the M complex signals at a first sampling rate to obtain M sampling data. It can be seen that M sampled data are formed by oversampling the detection device according to M complex signals. The receiving antenna i is one of N receiving antennas of the radar, and the first sampling rate shown in this embodiment is oversampling, that is, the first sampling rate may be any value greater than 1, for example, the first sampling rate may be 1.5 or 2. The sampling data acquired by the detection device through the first sampling rate comprises sampling time corresponding to each sampling point and frequency corresponding to each sampling point.
Step 305, the detection device performs a first dimension FFT on the sampled data to obtain a spectrum.
For better understanding, the following is exemplified with the first sampling rate being 2:
referring first to fig. 4a, fig. 4a shows sample data obtained after the detection apparatus samples the complex signal by a first sampling rate, where the sample data may be a waveform diagram as shown in fig. 4a, where a unit of an abscissa of the waveform diagram is a sampling time, a unit of the waveform diagram is microseconds (us), and the like, and a specific unit is not limited, and a unit of an ordinate of the waveform diagram is an amplitude.
The detection apparatus performs a first-dimension FFT process on the waveform diagram shown in fig. 4a to acquire a spectrum shown in fig. 4b, in which the frequency components of the complex signal are plotted on the abscissa and the amplitude is plotted on the ordinate.
In this embodiment, the first sampling rate is described as 2. The detection device samples the complex signal by a first sampling rate of 2 to obtain a frequency spectrum. The frequency spectrum generated by the detection means is divided into a positive frequency axis and a negative frequency axis. The amplitude corresponding to the signal reflected by the valid target appears on the positive frequency axis of the spectrum, while the amplitude corresponding to the noise appears on the negative frequency axis of the spectrum.
As can be seen, in this step, the detection apparatus may respectively obtain M corresponding frequency spectrums for M first echo signals received by the receiving antenna i.
Step 306, the detection device obtains a two-dimensional matrix corresponding to each receiving antenna.
For example, the detection device may obtain M frequency spectrums corresponding to M first echo signals received by the receiving antenna i, and the detection device may arrange the M frequency spectrums corresponding to the same receiving antenna i in a two-dimensional matrix. The two-dimensional matrix is a two-dimensional matrix corresponding to the receiving antenna i, and a specific process for obtaining the two-dimensional matrix is described as follows:
the detection means converts to distance for a target frequency in the target spectrum. The target frequency spectrum is any one of M frequency spectrums corresponding to the receiving antenna i, and the target frequency is one of a plurality of frequencies included in the target frequency spectrum. The detection device arranges the corresponding relation of the distance and the amplitude in a two-dimensional matrix. Wherein the distance and the amplitude both correspond to the target frequency.
Specifically, the detection device may convert the target frequency of the spectrum into a distance by:
and R is c f/2/S. Where c is the speed of light, S is the sweep spectrum of the radar, R is the target distance, and f is the frequency at which the radar transmits the probe signal.
The detection device can arrange the M first echo signals received by the receiving antenna i in the two-dimensional matrix corresponding to the receiving antenna i in the above mode. It can be seen that the detection apparatus may respectively create two-dimensional matrixes for the first echo signals received by different receiving antennas, for example, the radar has a receiving antenna 1 and a receiving antenna 2 … …, and the detection apparatus generates a two-dimensional matrix 1 for all the first echo signals received by the receiving antenna 1, and generates a two-dimensional matrix 2 … … for all the first echo signals received by the receiving antenna N.
The following illustrates an exemplary two-dimensional matrix corresponding to the receiving antenna i in conjunction with fig. 5:
if the receiving antenna i of the radar receives 6 first echo signals (i.e., the first echo signal 1, the first echo signal 2 … …, the first echo signal 6), the detection apparatus samples the sampling data converted by each first echo signal by 8 sampling points (i.e., sampling point 1, sampling point 2 … …, sampling point 8). The detection apparatus can acquire a two-dimensional matrix 501 as shown in fig. 5 for the reception antenna i.
According to a first behavior example included in the two-dimensional matrix, the detection device samples sampling data corresponding to the first echo signal according to the sampling point 1 to obtain a corresponding frequency, and then converts the frequency into the distance D1. The detection apparatus determines from the first echo signal 1 an amplitude F1 corresponding to the distance D1, and from the first echo signal 2 an amplitude F2 … … corresponding to the distance D1 and from the first echo signal 6a corresponding amplitude F6. The detection means may form a first row of the two-dimensional matrix based on the above parameters, i.e. the first row comprises the amplitudes of the distance D1 corresponding to the 6 first echo signals, respectively. By analogy, the detection device may obtain a two-dimensional matrix 501 as shown in fig. 5.
Step 307, the detection device determines a target two-dimensional matrix.
In this embodiment, when the detection device acquires a plurality of two-dimensional matrices corresponding to all receiving antennas of the radar, the detection device may determine the target two-dimensional matrix according to the plurality of two-dimensional matrices. Several alternative ways of acquiring the target two-dimensional matrix by the detection apparatus are described below:
mode 1
The detection device may determine that the target two-dimensional matrix is one of a plurality of two-dimensional matrices.
Mode 2
Taking the radar with N receiving antennas as an example, the detection device can obtain N two-dimensional matrices corresponding to the N receiving antennas respectively. The detection device can superpose the N two-dimensional matrixes to obtain the target two-dimensional matrix.
In this embodiment, a process of how the detection device superimposes the N two-dimensional matrices is not limited, for example, the detection device may average element values of all elements located in the same dimension in the N two-dimensional matrices to obtain a superimposed element value, where the superimposed element value is one element value included in the target two-dimensional matrix. For another example, the detection apparatus may obtain the superimposed element values by taking the modulus values of all the element values of the N two-dimensional matrices located in the same dimension and then averaging the modulus values. For another example, the detection apparatus determines the quantile as the superimposed element value among the element values of all elements located in the same dimension in the N two-dimensional matrices.
Step 308, the detection device determines at least one target distance in the target two-dimensional matrix.
In this embodiment, the detection device may estimate the noise according to the determined target distance, and the following describes a process of determining the target distance by the detection device:
the detection device determines a target distance in the target two-dimensional matrix, where the target distance is obtained by converting according to a negative frequency included in the frequency spectrum, and the description of the process of converting the negative frequency into the distance refers to the above-mentioned process of converting according to frequency into distance, which is not described in detail.
As shown in fig. 4b, the frequency spectrum shown in fig. 4b includes a positive frequency axis and a negative frequency axis, where the positive frequency axis includes a positive frequency whose value is positive, and the negative frequency axis includes a frequency whose value is negative. Due to the fact that the complex signals are subjected to oversampling, the frequency corresponding to the object to be tracked to be detected appears on the positive frequency axis, and the negative frequency axis of the frequency spectrum only has noise. The present embodiment can perform noise estimation based on the negative frequencies included in the negative frequency axis of the spectrum.
The target distance shown in this embodiment can be converted from any negative frequency on the negative frequency axis. The number of the determined target distances is not limited in this embodiment, and may be one or more.
Next, the detection device may determine target distances respectively corresponding to the at least one first negative frequency in the target two-dimensional matrix.
Step 309, the detection device determines a noise estimation matrix.
In this embodiment, the detection device determines the target amplitude according to the target distance in the determined target two-dimensional matrix. The detecting means may determine that an element value of a first element comprised by the noise estimate matrix is the target amplitude. In the case where the detecting means determines a plurality of target amplitudes, the element values of the plurality of first elements included in the noise estimation matrix are a plurality of target amplitudes, respectively. The following describes a process of determining the target amplitude according to the target distance by the detection device:
first, the detection device determines amplitudes respectively corresponding to the target distances in a two-dimensional matrix.
With continued reference to fig. 5, if the detecting device determines that the target distances are D1, D2, D3, and D4, the detecting device may determine that the amplitudes corresponding to D1 are F1, F2 … … F6 in the target two-dimensional matrix; the amplitudes corresponding to D2 are F1, F2 … … F6, and so on, and D4 is determined to correspond to F1, F2 … … F6.
Next, the detection apparatus determines the target amplitude according to the amplitudes corresponding to the target distances, respectively, and several alternative ways of determining are exemplarily described below:
mode 1
The detection device averages the amplitudes of the target distances respectively corresponding to the target two-dimensional matrixes to obtain the target amplitude. The specific averaging algorithm is not limited in this embodiment, and may be, for example, an arithmetic average, a geometric average, a square average, a harmonic average, or a weighted average.
Continuing with the above example as an example, the target amplitude corresponding to the target distance D1 determined by the detection apparatus is F1+ F2+ F3+ F4+ F5+ F6/6.
Mode 2
The detection device obtains the target amplitude by taking the modulus values of the amplitudes of the target distance respectively corresponding to the target distance in the target two-dimensional matrix and then averaging the modulus values.
Continuing with the above example, the detection apparatus determines the module value of the target amplitude F1+ the module value of F2+ the module value of F3+ the module value of F4+ the module value of F5+ the module value of F6/6 corresponding to the target distance D1.
Mode 2
The detection device determines the quantile to be the target amplitude in a plurality of amplitudes corresponding to the target two-dimensional matrix. The quantile may be a median, a quartile, or the like, and is not limited in this embodiment.
Continuing with the above example, if the detection device determines that the quantile is a median, the detection device may determine that the target amplitude corresponding to the target distance D1 is F4.
By analogy, the detection device can obtain a target amplitude corresponding to the target distance D2, a target amplitude corresponding to the target distance D3, and a target amplitude corresponding to the target distance D4. I.e. the target two-dimensional matrix 501 shown in fig. 5 is converted into the noise estimation matrix 502 shown in fig. 5. It can be seen that, in the example shown in fig. 5, the noise estimation matrix is a one-dimensional column matrix, and the noise estimation matrix includes a first element having an element value of the target amplitude corresponding to the target distance D1, a second element having an element value of the target amplitude corresponding to the target distance D2, and so on.
It should be clear that, in the present embodiment, the noise estimation matrix is taken as a column matrix for exemplary illustration, which is not limited, and in other examples, the noise estimation matrix may also be a row matrix.
Optionally, to improve the accuracy of noise estimation, the detection device may multiply a preset gain to the target amplitude. The present embodiment does not limit the magnitude of the preset gain, as long as after the target amplitude is multiplied by the preset gain, the obtained result can better conform to the amplitude of the noise.
Step 310, the detection device obtains a range-doppler matrix.
The detection device generates a range-Doppler matrix according to a plurality of echo signals received by the radar, and the detection of the object to be tracked is realized through the range-Doppler matrix.
Optionally, the detection apparatus shown in this embodiment may obtain the range-doppler matrix according to the first echo signal shown above. Namely, the detection device completes the acquisition of the noise estimation matrix and the acquisition of the range-Doppler matrix according to a plurality of first echo signals received by the radar.
Optionally, the second echo signal may be an echo signal different from the first echo signal. That is, the detection device acquires a noise estimation matrix for the first echo signal and acquires the to-be-detected distance-doppler matrix for the second echo signal. For example, the detection means may set the first processing cycle and the second processing cycle in advance. The present embodiment does not limit the specific duration of the first processing cycle and the second processing cycle, as long as the processing timing of the first processing cycle is earlier than the processing timing of the second processing cycle in the processing timing. The detection device may generate the noise estimation matrix based on a first echo signal received by the radar during a first processing period, and the detection device may generate the range-doppler matrix based on a second echo signal received by the radar during a second processing period. For the process of generating the range-doppler matrix, reference may be made to the above description of generating the range-doppler matrix according to the echo signal, which is not described in detail. In this embodiment, the detection apparatus obtains a noise estimation matrix based on the first echo signal, and the detection apparatus obtains a range-doppler matrix based on the second echo signal.
If the second echo signal can be an echo signal different from the first echo signal, the detection device may sample the complex signal of the first echo signal by a first sampling rate to obtain the noise estimation matrix, and may sample the complex signal of the second echo signal by a second sampling rate to obtain the range-doppler matrix. For a detailed description of the first sampling rate, please refer to the above description. The specific size of the second sampling rate is not limited in this embodiment, as long as the second sampling rate is also over-sampled.
Optionally, this embodiment takes the example that the second sampling rate is smaller than the first sampling rate as an example for explanation. The lower the second sampling rate is, the greater the ranging performance can be acquired by the detection device. In this embodiment, an example that the first sampling rate is 2 and the second sampling rate is 1 is taken as an example to describe how the detection apparatus obtains the range-doppler matrix is as follows:
firstly, converting each second echo signal received by a receiving antenna i of the radar into a complex signal by the detection device;
next, the detecting device samples each complex signal at a second sampling rate to obtain sampling data, in this embodiment, the second sampling rate is 1, for better understanding, it is shown in fig. 6a below, where fig. 6a shows the sampling data obtained after the detecting device samples the complex signal at the second sampling rate, where the sampling data may be a waveform shown in fig. 6 a.
Again, the detection apparatus performs a first-dimension FFT process on the waveform diagram shown in fig. 6a to acquire a spectrum as shown in fig. 6 b;
thirdly, the detection device acquires a two-dimensional matrix corresponding to the receiving antenna i, and performs second-dimensional FFT on the two-dimensional matrix to acquire a range-Doppler matrix;
finally, the detection device determines a range-doppler matrix. The range-doppler matrix may be one of range-doppler matrices corresponding to the plurality of receiving antennas, or the range-doppler matrix may be formed by overlapping the range-doppler matrices corresponding to the plurality of receiving antennas.
Step 311, the detection device performs a difference between the range-doppler matrix and the noise estimation matrix to obtain a target difference value.
The detection apparatus shown in this embodiment may obtain a target difference value of a second element of a target included in the range-doppler matrix by subtracting the range-doppler matrix from the noise estimation matrix in the following manner, where the second element of the target is any one of the second elements included in the range-doppler matrix, and several optional implementation manners of obtaining the target difference value by the detection apparatus are exemplarily described below:
mode 1
When the noise estimation matrix is a column matrix and the detecting device determines that the number of first elements included in the noise estimation matrix is equal to the number of second elements included in any column of the range-doppler matrix, the detecting device may make a difference between each column of elements included in the range-doppler matrix and the noise estimation matrix.
Specifically, when the number of first elements included in the noise estimation matrix is equal to the number of second elements included in any one column of the range-doppler matrix, the detection device may perform a difference between the column-by-column elements included in the range-doppler matrix and the noise estimation matrix to obtain a target difference value.
Optionally, if any column of the range-doppler matrix includes a plurality of second elements arranged in sequence from small to large according to the distance, and the noise estimation matrix also includes a plurality of first elements arranged in sequence from small to large according to the distance, the difference between each column of elements included in the range-doppler matrix and the noise estimation matrix by the detection device may specifically mean that the doppler velocity and the noise estimation value corresponding to the same distance in the range-doppler matrix and the noise estimation matrix are respectively subtracted.
Mode 2
When the noise estimation matrix is a column matrix and the detecting device determines that the number of first elements included in the noise estimation matrix is equal to the number of second elements included in any column of the range-doppler matrix, the range-doppler matrix is a two-dimensional matrix and the noise estimation matrix is a one-dimensional matrix. The detection device may copy the noise estimation matrix such that a matrix dimension of the copied noise estimation matrix is the same as a matrix dimension of the range-doppler matrix.
Specifically, the fact that the matrix dimension of the copied noise estimation matrix is the same as the matrix dimension of the range-doppler matrix means that the number of rows included in the copied noise estimation matrix is equal to the number of rows included in the range-doppler matrix, and the number of columns included in the copied noise estimation matrix is equal to the number of columns included in the range-doppler matrix.
The following is described with reference to specific examples: to improve the efficiency of the difference between the range-doppler matrix and the noise estimation matrix, see fig. 7, where 701 in fig. 7 is the noise estimation matrix obtained by the detection apparatus, and fig. 7 illustrates an example of the noise estimation matrix being a column matrix including 8 elements, the detection apparatus may duplicate the noise estimation matrix to form a duplicated noise estimation matrix 702. Specifically, each element included in any row of the copied noise estimation matrix has the same element value.
In the case that the noise estimation matrix and the range-doppler matrix have the same matrix dimension, the detection device may directly perform a difference between the noise estimation matrix and the range-doppler matrix to obtain the target difference value.
It should be clear that, in this example, the noise estimation matrix is taken as a column matrix for exemplary illustration, in other examples, the noise estimation matrix may also be a row matrix, and when the noise estimation matrix is a row matrix, please refer to the above description for describing a manner of difference between the range-doppler matrix and the noise estimation matrix, which is not repeated herein.
Mode 3
In the modes 1 and 2, taking the first sampling rate as 2 and the second sampling rate as 1 as an example, the number of elements included in the noise estimation matrix is the same as the number of elements included in any column of the range-doppler matrix, and in this mode, when the values of the first sampling rate and the second sampling rate are different from those of the above mode, the number of elements included in the noise estimation matrix is different from that of elements included in any column of the range-doppler matrix.
If the number of first elements included in the noise estimation matrix is greater than the number of second elements included in any row of the range-doppler matrix, the detection device reduces the noise estimation matrix so that the number of elements included in the noise estimation matrix is equal to the number of elements included in any row of the range-doppler matrix.
If the number of the first elements included in the noise estimation matrix is smaller than the number of the elements included in any row of the range-doppler matrix, the detection device expands the noise estimation matrix so that the number of the first elements included in the noise estimation matrix is equal to the number of the second elements included in any row of the range-doppler matrix.
In a case where the number of first elements included in the noise estimation matrix is equal to the number of second elements included in any column of the range-doppler matrix, the detection device may copy the noise estimation matrix to generate a copied noise estimation matrix, where the specific copying process is described in detail in the foregoing description and is not repeated.
For a description of a manner of making a difference between the range-doppler matrix and the noise estimation matrix, please refer to manner 2 in detail, which is not described in detail.
In step 312, the detecting device determines whether the target difference corresponding to the target second element is greater than or equal to the noise threshold, if not, step 313 is executed, and if so, step 314 is executed.
Under the condition that the detection device obtains the target difference value corresponding to the target second element, the detection device can judge whether the target difference value is larger than or equal to the noise threshold. The specific value of the noise threshold is not limited in this embodiment, and it is only required that the target second element is an object to be tracked, which needs to be tracked, when the target difference corresponding to the target second element is greater than or equal to the noise threshold. If the target difference value corresponding to the target second element is smaller than the noise threshold, it is determined that the target second element is a non-object to be tracked, and the target object is more likely to be noise.
Optionally, the detection device may obtain the noise threshold in advance according to a false alarm probability, where the false alarm probability refers to a probability that, in a radar detection process, due to ubiquitous and fluctuating noise, an object to be tracked does not exist actually but is determined as an object to be tracked.
The following describes the process of determining the noise threshold: it should be clear that the description of the determination method of the noise threshold in this embodiment is an optional example, and is not limited:
optionally, the detecting device may determine a noise threshold for the range-doppler matrix, for example, select any second element in the range-doppler matrix, determine a noise threshold corresponding to the second element, and use the noise threshold for noise estimation of all second elements of the range-doppler matrix.
Optionally, the detecting device may determine a corresponding noise threshold for each second element in the range-doppler matrix. The following description is given by taking as an example how the detection apparatus determines the corresponding noise threshold for the target second element:
the detection device can calculate the noise threshold corresponding to the target second element through the following formula:
noise threshold being TC-30logRWhere TC is a predetermined constant, and R is a corresponding distance of the second element of the target in the range-doppler matrix.
Step 313, the detection device determines that the target second element is a non-object to be tracked.
Specifically, when the detection device determines that the target difference value corresponding to the target second element is smaller than the noise threshold, it indicates that the target second element is not a non-to-be-tracked object that needs to be tracked, that is, the detection device does not need to track the target second element.
And step 314, the detection device determines that the target second element is the object to be tracked.
Specifically, when the detection device determines that the target difference corresponding to the target second element is greater than or equal to the noise threshold, it indicates that the target second element is the detected object to be tracked.
Step 315, the detection device tracks the object to be tracked.
Specifically, all the information to be followed included in the range-doppler matrix acquired by the detection deviceWhen the object is tracked, the detection device acquires the distance and the Doppler velocity respectively corresponding to all the objects to be tracked included in the distance-Doppler matrix, and the detection device respectively corresponds to all the objects to be trackedDistance and Doppler velocityAnd performing third FTT processing to acquire the angle of each object to be tracked.
The detection device can determine point cloud data, wherein the point cloud data comprises distances, Doppler speeds and angles corresponding to all objects to be tracked included in the distance-Doppler matrix. The detection device can realize the tracking of each object to be tracked according to the distance, the Doppler velocity and the angle of each object to be tracked.
Therefore, based on the method shown in the embodiment, the object to be tracked can be directly determined according to the range-doppler matrix and the noise estimation matrix. Compared with the sliding window CFAR detection method in the process of detecting the object to be tracked, the efficiency of detecting the object to be tracked is effectively improved.
Moreover, by adopting the method shown in the embodiment, the noise estimation matrix can be determined according to the target two-dimensional matrix, and the distance-doppler matrix can realize the detection of the object to be tracked according to the noise estimation matrix, without judging the size of the parameter corresponding to the adjacent element of the object to be tracked in the distance-doppler matrix to detect the object to be tracked. That is, in the detection process of the object to be tracked by the method in this embodiment, when determining whether a certain element in the range-doppler matrix is the object to be tracked, it is not necessary to perform determination according to other elements adjacent to the certain element, but it is directly determined whether the second element is the object to be tracked by directly subtracting the second element value of the certain element from the element value of the first element in the noise estimation matrix, so that the method in this embodiment does not cause a situation in which a strong object to be tracked covers a weak object to be tracked. According to the method, even if a plurality of adjacent objects to be tracked exist in the range-Doppler matrix, the detection device can accurately detect the objects to be tracked, and the accuracy of detecting the objects to be tracked is improved.
Based on the detection method shown in fig. 3, the following fig. 8 illustrates an exemplary description of how to effectively improve the efficiency and accuracy of detecting the object to be tracked:
step 801, the detection device obtains a signal to be detected received by each receiving antenna from the radar.
In this embodiment, in order to improve the accuracy of detecting the object to be tracked, before the process of detecting the object to be tracked is executed, the detection device needs to detect whether there is an interference signal that interferes with the radar at present, and only when the detection device determines that there is no interference signal that interferes with the radar, the detection device can accurately detect the object to be tracked, and the following description is first given to the interference signal:
there are generally two parameters that affect the accuracy of the detection device in detecting the object to be tracked, one is noise and the other is interference. In the embodiment shown in fig. 3, if the target spectrum is obtained from the complex signal, the noise included in the complex signal is uniformly distributed on the positive and negative frequency axes. While the interference signal is different from the noise, the interference is usually an electromagnetic wave signal from radar emission on other vehicles. Typically, the radar installed on the opposite vehicle emits electromagnetic wave signals to the radar of the vehicle. The electromagnetic wave signals sent to the vehicle are not echo signals formed by the reflection of electromagnetic waves emitted by the radar of the vehicle through a real object, and interference is brought to the subsequent processing of the radar of the vehicle by receiving the signals. In the present embodiment, an electromagnetic wave signal emitted from a radar on another vehicle is defined as an interference signal whose frequency band, chirp rate, and the like are different from those of the own-vehicle radar.
The detection device shown in this embodiment detects whether the radar of the vehicle receives an interference signal currently, the detection device may control the radar not to send a detection signal to the surrounding environment, at this time, a receiving antenna i of the radar is in an on state, that is, the radar receives a signal to be detected from the surrounding environment through the receiving antenna i, if the detection device determines that the signal to be detected is an interference signal, the detection device may determine that the radar is interfered, and the specific description of the receiving antenna i shown in this embodiment is please refer to the embodiment shown in fig. 3 in detail, which is not repeated.
Step 802, the detection device obtains a frequency spectrum to be detected of the signal to be detected.
Please refer to the specific process of the detecting device acquiring the frequency spectrum of the echo signal received by the receiving antenna i in steps 301 to 305 shown in fig. 3, which is not described in detail herein.
Step 803, the detection device determines whether the amplitude corresponding to any negative frequency of the negative frequency axis of the to-be-detected frequency spectrum of the to-be-detected signal is less than or equal to the interference threshold, if so, step 804 is executed.
In the process of acquiring the frequency spectrum to be detected by the detection device, in this embodiment, taking an example that the detection device samples the complex signal corresponding to the receiving antenna i by using a sampling rate whose value is 2, the target frequency spectrum acquired by the detection device may be as shown in fig. 4b, which is not described in detail.
In this embodiment, if the detection device determines that the amplitude corresponding to any negative frequency included in the negative frequency axis of the frequency spectrum to be detected is less than or equal to the interference threshold, it indicates that the receiving antenna i does not receive the interference signal, and if the detection device determines that the amplitude corresponding to any negative frequency included in the negative frequency axis of the frequency spectrum to be detected is less than the interference threshold, it indicates that the receiving antenna i has received the interference signal. The presence of the interference signal can seriously reduce the accuracy of the detection device for detecting the object to be tracked.
In this embodiment, if the radar of the vehicle is not interfered, the waveform of the spectrum to be measured corresponding to the receiving antenna i of the radar of the vehicle may be as shown in fig. 4b, that is, there is no abnormal high energy in the spectrum to be measured, and specifically, the amplitude corresponding to any negative frequency on the negative frequency axis of the target spectrum is less than or equal to the interference threshold.
If the radar of the vehicle is interfered, the negative frequency axis of the frequency spectrum to be detected corresponding to the receiving antenna i of the radar of the vehicle has abnormal high energy, which is particularly embodied that the amplitude corresponding to one or more negative frequencies on the negative frequency axis of the frequency spectrum to be detected is larger than or equal to the interference threshold.
It can be seen that, when the detection device detects that the negative frequency axis of the frequency spectrum to be detected includes one or more negative frequencies, corresponding amplitudes of which are greater than the interference threshold, the detection device determines that the receiving antenna i of the radar receives the interference signal. If the detection device detects that the amplitudes corresponding to all negative frequencies included in the frequency spectrum to be detected are less than or equal to the interference threshold, the detection device determines that the receiving antenna i of the radar does not receive the interference signal, and then the execution of step 804 is triggered.
Step 804, the detection device acquires a time-frequency signal from the radar.
Step 805, the detecting device converts the time frequency signal into a complex signal.
Step 806, the detecting device performs over-sampling on each complex signal to obtain sampled data.
In step 807, the detection apparatus performs a first-dimension FFT on the sampled data to obtain a spectrum.
Step 808, the detection device obtains a two-dimensional matrix corresponding to each receiving antenna.
The process from step 804 to step 808 in this embodiment is shown in detail in step 301 to step 306 in fig. 3, which is not described in detail.
Step 809, the detecting device performs a second-dimension FFT on the plurality of two-dimensional matrices respectively to obtain a plurality of range-doppler matrices.
In a case where the detection device acquires one two-dimensional matrix for each receiving antenna of the radar, the detection device may perform a second-dimensional FFT on each two-dimensional matrix to acquire a range-doppler matrix corresponding to each receiving antenna, and for a specific description of the range-doppler matrix, please refer to the above embodiment in detail, which is not repeated in detail.
Step 810, the detection device determines a target range-doppler matrix.
In this embodiment, when the detection device acquires a plurality of range-doppler matrices respectively corresponding to all receiving antennas of the radar, the detection device may determine a target range-doppler matrix according to the plurality of range-doppler matrices. The description of determining the target range-doppler matrix in the multiple range-doppler matrices shown in this embodiment may refer to step 307 shown in fig. 3, and details of the process of determining the target two-dimensional matrix in the multiple two-dimensional matrices are not repeated in this embodiment.
Step 811, the detection device determines at least one target range in the target range-doppler matrix.
The detailed description of step 811 shown in this embodiment can be referred to as step 308 shown in fig. 3, and is not repeated herein. The difference between step 811 and step 308 is that: in fig. 3, the two-dimensional matrix of the target corresponds to the target distance, which is the amplitude, and the two-dimensional matrix of the target corresponds to the target distance, which is the doppler velocity, as shown in this step 811.
Step 812, the detection device determines a noise estimation matrix.
In this embodiment, the detection device determines the target doppler velocity according to the target distance in the determined target distance-doppler matrix. The detecting device can determine that the element value of a first element included in the noise estimation matrix is the target doppler velocity. When the detection device determines a plurality of target doppler velocities, the element values of the first elements included in the noise estimation matrix are a plurality of target doppler velocities, respectively. For a specific description of the process of determining the target doppler velocity by the detection device according to the target distance in this step, refer to the process of determining the target amplitude by the detection device according to the target distance in step 309, which is not described in detail.
Step 813, the detection device determines a range-doppler matrix.
In step 814, the detection device performs a difference between the range-doppler matrix and the noise estimation matrix to obtain a target difference value.
Step 815, the detecting device determines whether the target difference corresponding to the target second element is greater than or equal to the noise threshold, if not, step 816 is executed, and if so, step 817 is executed.
Step 816, the detection apparatus determines that the target second element is a non-object to be tracked.
817, the detection device determines that the target second element is the object to be tracked.
Step 818, the detection device tracks the object to be tracked.
For details of the specific execution process from step 814 to step 818 shown in this embodiment, please refer to step 310 to step 315 shown in fig. 3, which is not repeated herein.
By adopting the method shown in the embodiment, the detection device can detect the object to be tracked only when no interference signal causing interference to the radar exists at present, so that the situation that the detection device performs false detection on the object to be tracked due to the existence of the interference signal is avoided, and the accuracy of detecting the object to be tracked is effectively improved. In this embodiment, a range-doppler matrix generated according to a first echo signal is processed to obtain a noise estimation matrix, so that an element value of a first element included in the obtained noise estimation matrix is closer to noise, thereby improving accuracy of detecting an object to be tracked.
The following fig. 9 illustrates how to effectively improve the robustness of the noise estimation:
step 901, the detection device obtains a signal to be detected received by each receiving antenna from the radar.
Step 902, the detection device obtains a frequency spectrum to be detected of the signal to be detected.
Step 903, the detection device determines whether the amplitude corresponding to any negative frequency of the negative frequency axis of the frequency spectrum to be detected of the signal to be detected is less than or equal to the interference threshold, if so, step 904 is executed.
Please refer to steps 801 to 803 in fig. 8 in detail in steps 901 to 903 in this embodiment, which are not described in detail.
Step 904, the detection device acquires a time-frequency signal from the radar in a first processing period.
In this embodiment, the detection device may determine a first processing period for noise estimation in advance, and the detection device acquires the time-frequency signal from the radar only within the duration of the first processing period. The duration of the first processing period is not limited in this embodiment.
Step 905, the detecting device converts the time frequency signal into a complex signal.
Step 906, the detecting device performs over-sampling on each complex signal to obtain sampled data.
Step 907, the detection device performs a first dimension FFT on the sampled data to obtain a spectrum.
Step 908, the detection device obtains a two-dimensional matrix corresponding to each receiving antenna.
In step 909, the detection apparatus performs a second-dimension FFT on the plurality of two-dimensional matrices to obtain a plurality of range-doppler matrices.
Step 910, the detection device determines a target range-doppler matrix.
Step 911, the detection device determines at least one target range in the target range-doppler matrix.
Step 912, the detection device determines a noise estimation matrix.
Please refer to steps 801 to 812 shown in fig. 8, where the specific execution process of steps 904 to 912 shown in this embodiment is not described in detail in this embodiment.
Step 913, the detection device determines the range-doppler matrix in the second processing cycle.
In this embodiment, the detection device may be preset with a second processing period, and the detection device determines the range-doppler matrix according to the second echo signal only in the second processing period. If the detecting device determines that the timing of the second processing period is exceeded, the detecting device may return to perform step 901 or return to perform step 904 for reacquiring the noise estimation matrix.
The present embodiment does not limit the specific duration of the first processing cycle and the second processing cycle, as long as the processing timing of the first processing cycle is earlier than the processing timing of the second processing cycle in the processing timing. In this embodiment, a specific correspondence relationship between the first processing cycle and the second processing cycle is not limited, as long as one first processing cycle corresponds to one or more second processing cycles.
For example, the first processing period and the second processing period may be in a one-to-one correspondence relationship, that is, a noise estimation matrix acquired by the detection apparatus in the first processing period is used for performing noise estimation on the time-frequency signal acquired by the detection apparatus in the second processing period.
For another example, the first processing period and the second processing period may be in a one-to-many correspondence relationship, that is, a noise estimation matrix obtained by the detection device in the first processing period is used to perform noise estimation on the time-frequency signals obtained by the detection device in a plurality of subsequent second processing periods, respectively.
Step 914, the detection device performs a difference between the range-doppler matrix and the noise estimation matrix to obtain a target difference value.
Step 915, the detecting device determines whether the target difference corresponding to the target second element is greater than or equal to the noise threshold, if not, step 916 is executed, and if yes, step 917 is executed.
Step 916, the detection device determines that the target second element is a non-object to be tracked.
Step 917, the detection device determines the target second element as the object to be tracked.
Step 918, the detection device tracks the object to be tracked in the second processing cycle.
The specific processes shown in step 913 to step 918 in this embodiment are shown in step 813 to step 818 in fig. 8, and the specific implementation process is not described again.
Since the noise of a radar generally varies with the temperature of the radar, which is generally related to the operating time of the radar. Therefore, in the radar working process, the duration of the first processing period and the duration of the second processing period can be allocated according to a certain proportion, so that the robustness of the noise estimation matrix estimated by the processor is ensured.
The above embodiments describe the detection method provided in the present application in detail, and the following describes the structure of a detection apparatus for performing the detection method shown in the above embodiments:
referring to fig. 10, fig. 10 is a structural example diagram of an embodiment of the detecting device provided by the present invention. The detection apparatus 1000 shown in the present embodiment includes an acquisition unit 1001 and a processing unit 1002.
An obtaining unit 1001 configured to obtain a frequency spectrum corresponding to a first echo signal received by a radar, where a negative frequency axis of the frequency spectrum is composed of a frequency of noise and a corresponding amplitude;
a processing unit 1002, configured to determine a noise estimation matrix according to a negative frequency axis of the frequency spectrum, where the noise estimation matrix includes an element that takes the value of the amplitude or a doppler velocity converted from the amplitude; acquiring a range-Doppler matrix according to the first echo signal or the second echo signal received by the radar; and determining one element of the elements of the range-Doppler matrix, wherein the difference value between the element value and one element value in the noise estimation matrix is greater than or equal to a noise threshold, as the object to be tracked.
Optionally, the obtaining unit 1001 is specifically configured to:
acquiring a plurality of signals, wherein the plurality of signals are formed by converting the first echo signal;
oversampling the complex signal to obtain sampled data;
a first dimension fast fourier transform, FFT, is performed on the sampled data to obtain the spectrum.
Optionally, the radar has a plurality of receiving antennas, each receiving antenna is configured to receive a plurality of the first echo signals, and the processing unit 1002 is specifically configured to, in the process of determining the noise estimation matrix according to at least part of the negative frequencies of the negative frequency axis of the frequency spectrum:
acquiring a plurality of initial two-dimensional matrixes, wherein the initial two-dimensional matrixes respectively correspond to the receiving antennas, and the initial two-dimensional matrixes comprise the frequency spectrums of the first echo signals received by the corresponding receiving antennas;
determining a target two-dimensional matrix, wherein the target two-dimensional matrix is one of the plurality of initial two-dimensional matrices, or the target two-dimensional matrix is formed by superposing the plurality of initial two-dimensional matrices;
and determining the noise estimation matrix according to the target two-dimensional matrix.
Optionally, in the process of determining the noise estimation matrix according to the target two-dimensional matrix, the processing unit is specifically configured to:
determining a target distance in the target two-dimensional matrix, wherein the target distance is converted according to the negative frequency included by the frequency spectrum;
determining a target amplitude corresponding to the target distance in the target two-dimensional matrix;
determining the noise estimation matrix, wherein the noise estimation matrix comprises elements which take the values of the target amplitude.
Optionally, in the process of determining the target amplitude corresponding to the target distance in the target two-dimensional matrix, the processing unit 1002 is specifically configured to:
and averaging the amplitudes of the target distances respectively corresponding to the target two-dimensional matrix to obtain the target amplitude.
Optionally, the processing unit 1002 is further configured to:
respectively performing second-dimensional FFT on the initial two-dimensional matrixes to obtain a plurality of initial distance-Doppler matrixes;
determining a target range-doppler matrix, wherein the target range-doppler matrix is one of the plurality of initial range-doppler matrices, or the target range-doppler matrix is formed by overlapping the plurality of initial range-doppler matrices;
the noise estimation matrix is determined from the target range-doppler matrix.
Optionally, in the process of determining the noise estimation matrix according to the target range-doppler matrix, the processing unit 1002 is specifically configured to:
determining a target distance in the target distance-Doppler matrix, wherein the target distance is converted according to the negative frequency included in the frequency spectrum;
determining a target Doppler velocity corresponding to the target distance in the target distance-Doppler matrix;
determining the noise estimation matrix, wherein the noise estimation matrix comprises an element which takes the value of the target Doppler velocity.
Optionally, in the process that the processing unit 1002 determines the doppler velocity corresponding to the target distance in the target distance-doppler matrix, the processing unit 1002 is specifically configured to:
averaging the Doppler speeds respectively corresponding to the target distance in the target distance-Doppler matrix to obtain the target Doppler speed.
Optionally, the processing unit 1002 is further configured to:
and when the dimension of the noise estimation matrix is not equal to the dimension of the range-Doppler matrix, processing the dimension of the noise estimation matrix, wherein the processed dimension of the noise estimation matrix is equal to the dimension of the range-Doppler matrix.
Optionally, the obtaining unit 1001 is further configured to:
acquiring a plurality of signals to be detected, wherein the plurality of signals to be detected are formed by converting the signals to be detected;
oversampling the data signal to be detected to obtain sampling data to be detected;
performing first-dimension FFT on the sampling data to be detected to obtain a frequency spectrum to be detected;
and determining that the amplitude corresponding to any negative frequency included in the negative frequency axis of the frequency spectrum to be detected is less than or equal to the interference threshold.
In one implementation, the detection apparatus 1000 may be disposed in a radar, and in another implementation, the detection apparatus 1000 may also be disposed separately from the radar. The detection apparatus 1000 comprises respective units for performing respective operations and/or processes performed by the detection apparatus in the respective method embodiments.
In one implementation manner, a module of the obtaining unit 1001 and a module of the processing unit 1002, which are included in the detecting apparatus 1000, that perform a transceiving function may be a transceiver, and a module of the obtaining unit 1001 and the processing unit 1002 that perform a processing function may be a processor. Wherein the transceiver has a transmitting and/or receiving function, the transceiver can also be replaced by a receiver and/or a transmitter.
In another implementation, the detection device 1000 may be a chip or an integrated circuit. At this time, the acquisition unit 1001 and the processing unit 1002 may be logic circuits.
In one implementation, the processing unit 1002 may be a processing device, and the functions of the processing device may be partially or wholly implemented by software.
Alternatively, the functions of the processing means may be partly or wholly implemented by software. At this time, the processing device may include a memory for storing the computer program and a processor for reading and executing the computer program stored in the memory to perform the corresponding processes and/or steps in any one of the method embodiments.
Alternatively, the processing means may comprise only a processor. The memory for storing the computer program is located outside the processing device and the processor is connected to the memory by means of circuits/wires to read and execute the computer program stored in the memory.
Alternatively, the functions of the processing means may be partly or wholly implemented by hardware. At this time, the processing device may include an input interface circuit, a logic circuit, and an output interface circuit.
For example, the processing device may be one or more field-programmable gate arrays (FPGAs), Application Specific Integrated Circuits (ASICs), system on chips (socs), Central Processing Units (CPUs), Network Processors (NPs), digital signal processing circuits (DSPs), Micro Controllers (MCUs), Programmable Logic Devices (PLDs), or other integrated chips, or any combination of the above chips or processors.
In addition, the present application also provides an electronic device, which is described below with reference to fig. 11:
referring to fig. 11, fig. 11 is a diagram illustrating a structure of an embodiment of an electronic device according to the present invention. As shown in fig. 11, the electronic device 1100 includes a processor 1101, a transceiver 1102, and a memory 1003. The processor 1101, the transceiver 1102 and the memory 1103 can communicate with each other through the internal connection path to transmit control signals and/or data signals. The memory 1103 is used for storing a computer program, and the processor 1101 is used for calling and running the computer program from the memory 1103 to control the transceiver 1102 to transmit and receive signals.
Alternatively, the processor 1101 and the memory 1103 may be combined into one processing device, and the processor 1101 is configured to execute the program code stored in the memory 1103 to implement the above-mentioned functions.
Optionally, the memory 1103 may also be integrated in the processor 1101. Alternatively, the memory 1103 is separate from the processor 1101, i.e., external to the processor 1101.
The processor 1101 may be used to perform the actions implemented by the detection means described in the previous method embodiments. The transceiver 1102 can be configured to perform receive or transmit actions performed by the detection apparatus, and the memory 1103 can be configured to implement the stored functions.
Optionally, the electronic device 1100 may also include a power supply 1105 for providing power to various devices or circuits within the electronic device 1100.
In addition, in order to further improve the functions of the electronic device 1100, the electronic device 1100 may further include one or more of an input unit 1106, a display unit 1107, a sensor 1110, and the like.
Alternatively, the input unit 1106 may be a signal input interface, and the display unit 1107 may also be a signal output interface.
In addition, the application also provides a detection system, which comprises the detection device and the radar in the method embodiments of the application.
The present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer, causes the computer to perform the operations and/or processes performed by the detection apparatus in any one of the method embodiments.
The present application further provides a computer program product comprising computer program code which, when run on a computer, causes the computer to perform the operations and/or processes performed by the detection apparatus in any of the method embodiments.
The present application further provides a chip comprising a processor. A memory for storing the computer program is provided separately from the chip, and a processor is used for executing the computer program stored in the memory to perform the operations and/or processes performed by the detection apparatus in any one of the method embodiments.
Further, the chip may also include a memory and/or a communication interface. The communication interface may be an input-output interface, an input-output circuit, or the like.
The processor referred to in the embodiments above may be an integrated circuit chip having the capability of processing signals. In implementation, the steps of the above method embodiments may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The processor may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, or discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in the embodiments of the present application may be directly implemented by a hardware encoding processor, or implemented by a combination of hardware and software modules in the encoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the unit is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (22)

  1. A detection method for detecting an object to be tracked, the method comprising:
    acquiring a frequency spectrum corresponding to a first echo signal received by a radar, wherein a negative frequency axis of the frequency spectrum consists of the frequency of noise and corresponding amplitude;
    determining a noise estimation matrix according to the negative frequency axis of the frequency spectrum, wherein the noise estimation matrix comprises an element taking the value of the amplitude or an element of the Doppler velocity converted from the amplitude;
    acquiring a range-Doppler matrix according to the first echo signal or a second echo signal received by the radar;
    and determining one element of the elements of the range-Doppler matrix, wherein the difference value between the element value and one element value in the noise estimation matrix is greater than or equal to a noise threshold, as the object to be tracked.
  2. The method of claim 1, wherein the obtaining a spectrum corresponding to the first echo signal received by the radar comprises:
    acquiring a complex signal, wherein the complex signal is formed by converting the first echo signal;
    oversampling the complex signal to obtain sampled data;
    and performing a first-dimension Fast Fourier Transform (FFT) on the sampling data to acquire the frequency spectrum.
  3. The detection method according to claim 1 or 2, wherein the radar has a plurality of receiving antennas, each receiving antenna being configured to receive a plurality of the first echo signals, and wherein the determining a noise estimation matrix from at least part of the negative frequencies of the negative frequency axis of the frequency spectrum comprises:
    obtaining a plurality of initial two-dimensional matrixes, wherein the initial two-dimensional matrixes respectively correspond to the receiving antennas, and the initial two-dimensional matrixes comprise the frequency spectrums of the first echo signals received by the corresponding receiving antennas;
    determining a target two-dimensional matrix, wherein the target two-dimensional matrix is one of the plurality of initial two-dimensional matrices, or the target two-dimensional matrix is formed by superposing the plurality of initial two-dimensional matrices;
    and determining the noise estimation matrix according to the target two-dimensional matrix.
  4. The detection method of claim 3, wherein the determining the noise estimation matrix from the target two-dimensional matrix comprises:
    determining a target distance in the target two-dimensional matrix, wherein the target distance is converted according to the negative frequency included by the frequency spectrum;
    determining a target amplitude corresponding to the target distance in the target two-dimensional matrix;
    determining the noise estimation matrix, wherein the noise estimation matrix comprises elements taking the values as the target amplitudes.
  5. The detection method according to claim 4, wherein said determining a target amplitude corresponding to the target distance in the target two-dimensional matrix comprises:
    and averaging the amplitudes of the target distances respectively corresponding to the target two-dimensional matrixes to obtain the target amplitude.
  6. The detection method according to claim 3, wherein after said obtaining a plurality of initial two-dimensional matrices, the method further comprises:
    respectively performing second-dimension FFT on the initial two-dimension matrixes to obtain a plurality of initial range-Doppler matrixes;
    determining a target range-doppler matrix, wherein the target range-doppler matrix is one of the plurality of initial range-doppler matrices, or the target range-doppler matrix is formed by superposing the plurality of initial range-doppler matrices;
    and determining the noise estimation matrix according to the target distance-Doppler matrix.
  7. The detection method of claim 6, wherein the determining the noise estimation matrix from the target range-Doppler matrix comprises:
    determining a target distance in the target distance-Doppler matrix, wherein the target distance is converted according to the negative frequency included in the frequency spectrum;
    determining a target Doppler velocity corresponding to the target distance in the target distance-Doppler matrix;
    and determining the noise estimation matrix, wherein the noise estimation matrix comprises elements which take the values of the target Doppler velocity.
  8. The detection method according to claim 7, wherein the determining the Doppler velocity corresponding to the target range in the target range-Doppler matrix comprises:
    averaging the Doppler speeds respectively corresponding to the target distances in the target distance-Doppler matrix to obtain the target Doppler speed.
  9. The detection method according to any one of claims 1 to 8, wherein before the determining the object to be tracked, the method further comprises:
    and when the dimension of the noise estimation matrix is not equal to the dimension of the range-Doppler matrix, processing the dimension of the noise estimation matrix, wherein the processed dimension of the noise estimation matrix is equal to the dimension of the range-Doppler matrix.
  10. The method according to any one of claims 1 to 9, wherein before the obtaining of the spectrum corresponding to the first echo signal received by the radar, the method further comprises:
    acquiring a signal to be detected from the radar, wherein the signal to be detected is a signal received by the radar from the surrounding environment;
    acquiring a complex signal to be detected, wherein the complex signal to be detected is formed by converting the signal to be detected;
    oversampling the data signal to be detected to obtain sampling data to be detected;
    performing first-dimension FFT on the sampling data to be detected to obtain a frequency spectrum to be detected;
    and determining that the amplitude corresponding to any negative frequency included in the negative frequency axis of the frequency spectrum to be detected is less than or equal to an interference threshold.
  11. A detection apparatus for detecting an object to be tracked, comprising:
    the radar comprises an acquisition unit, a detection unit and a processing unit, wherein the acquisition unit is used for acquiring a frequency spectrum corresponding to a first echo signal received by a radar, and a negative frequency axis of the frequency spectrum consists of the frequency of noise and corresponding amplitude;
    a processing unit, configured to determine a noise estimation matrix according to a negative frequency axis of the frequency spectrum, where the noise estimation matrix includes an element whose value is the amplitude or an element of doppler velocity converted from the amplitude; acquiring a range-Doppler matrix according to the first echo signal or a second echo signal received by the radar; and determining one element of the elements of the range-Doppler matrix, wherein the difference value between the element value and one element value in the noise estimation matrix is greater than or equal to a noise threshold, as the object to be tracked.
  12. The detection apparatus according to claim 11, wherein the obtaining unit is specifically configured to:
    acquiring a complex signal, wherein the complex signal is formed by converting the first echo signal;
    oversampling the complex signal to obtain sampled data;
    and performing a first-dimension Fast Fourier Transform (FFT) on the sampling data to acquire the frequency spectrum.
  13. The detection apparatus according to claim 11 or 12, wherein the radar has a plurality of receiving antennas, each receiving antenna being configured to receive a plurality of the first echo signals, and wherein the processing unit, in determining the noise estimation matrix from at least part of the negative frequencies of the negative frequency axis of the frequency spectrum, is configured to:
    obtaining a plurality of initial two-dimensional matrixes, wherein the initial two-dimensional matrixes respectively correspond to the receiving antennas, and the initial two-dimensional matrixes comprise the frequency spectrums of the first echo signals received by the corresponding receiving antennas;
    determining a target two-dimensional matrix, wherein the target two-dimensional matrix is one of the plurality of initial two-dimensional matrices, or the target two-dimensional matrix is formed by superposing the plurality of initial two-dimensional matrices;
    and determining the noise estimation matrix according to the target two-dimensional matrix.
  14. The detection apparatus according to claim 13, wherein the processing unit, in determining the noise estimation matrix according to the target two-dimensional matrix, is specifically configured to:
    determining a target distance in the target two-dimensional matrix, wherein the target distance is converted according to the negative frequency included by the frequency spectrum;
    determining a target amplitude corresponding to the target distance in the target two-dimensional matrix;
    determining the noise estimation matrix, wherein the noise estimation matrix comprises elements taking the values as the target amplitudes.
  15. The detection apparatus according to claim 14, wherein the processing unit, in the process of determining the target amplitude corresponding to the target distance in the target two-dimensional matrix, is specifically configured to:
    and averaging the amplitudes of the target distances respectively corresponding to the target two-dimensional matrixes to obtain the target amplitude.
  16. The detection device of claim 13, wherein the processing unit is further configured to:
    respectively performing second-dimension FFT on the initial two-dimension matrixes to obtain a plurality of initial range-Doppler matrixes;
    determining a target range-doppler matrix, wherein the target range-doppler matrix is one of the plurality of initial range-doppler matrices, or the target range-doppler matrix is formed by superposing the plurality of initial range-doppler matrices;
    and determining the noise estimation matrix according to the target distance-Doppler matrix.
  17. The detection apparatus according to claim 16, wherein the processing unit, in the process of determining the noise estimation matrix according to the target range-doppler matrix, is specifically configured to:
    determining a target distance in the target distance-Doppler matrix, wherein the target distance is converted according to the negative frequency included in the frequency spectrum;
    determining a target Doppler velocity corresponding to the target distance in the target distance-Doppler matrix;
    and determining the noise estimation matrix, wherein the noise estimation matrix comprises elements which take the values of the target Doppler velocity.
  18. The detection apparatus according to claim 17, wherein the processing unit, in the process of determining the doppler velocity corresponding to the target distance in the target distance-doppler matrix, is specifically configured to:
    averaging the Doppler speeds respectively corresponding to the target distances in the target distance-Doppler matrix to obtain the target Doppler speed.
  19. The detection apparatus according to any one of claims 11 to 18, wherein the processing unit is further configured to:
    and when the dimension of the noise estimation matrix is not equal to the dimension of the range-Doppler matrix, processing the dimension of the noise estimation matrix, wherein the processed dimension of the noise estimation matrix is equal to the dimension of the range-Doppler matrix.
  20. The detection apparatus according to any one of claims 11 to 19, wherein the obtaining unit is further configured to:
    acquiring a complex signal to be detected, wherein the complex signal to be detected is formed by converting the signal to be detected;
    oversampling the data signal to be detected to obtain sampling data to be detected;
    performing first-dimension FFT on the sampling data to be detected to obtain a frequency spectrum to be detected;
    and determining that the amplitude corresponding to any negative frequency included in the negative frequency axis of the frequency spectrum to be detected is less than or equal to an interference threshold.
  21. An electronic device for detecting an object to be tracked, comprising a transceiver, a memory for storing a computer program, and a processor for reading and executing the computer program stored in the memory to perform the method according to any one of claims 1-10.
  22. A computer-readable storage medium, in which a computer program is stored which, when executed on a computer, causes the computer to carry out the method according to any one of claims 1-10.
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