CN110907930B - Vehicle-mounted radar target detection and estimation method and device based on angle estimation - Google Patents
Vehicle-mounted radar target detection and estimation method and device based on angle estimation Download PDFInfo
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- 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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
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- G01S13/50—Systems of measurement based on relative movement of target
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- 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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
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Abstract
The invention discloses a vehicle-mounted radar target detection and estimation method and device based on angle estimation, wherein the method comprises the following steps: s1, obtaining an echo signal of a target radar and carrying out angle estimation to obtain angle spectrum data; s2, carrying out graying processing on the angle spectrum data obtained in the step S1 to preliminarily detect a target higher than a preset threshold value as a real target so as to obtain grayed angle spectrum data; s3, performing target detection on the gray scale angle spectrum data obtained in the step S2 to obtain a final target detection result and outputting the final target detection result; the device comprises an angle estimation module, a primary target detection module and a secondary target detection module. The method has the advantages of simple implementation method, low cost, good weak target detection performance, low omission factor and false alarm rate and the like.
Description
Technical Field
The invention relates to the technical field of vehicle-mounted radar detection, in particular to a vehicle-mounted radar target detection estimation method and device based on angle estimation.
Background
In order to realize active safety of a vehicle, a radar is generally installed in the vehicle to realize collision avoidance, blind area detection and the like. Typical like millimeter wave radar sensor, owing to can work in all weather, can satisfy the requirement of vehicle to the adaptability of all-day weather, and do not receive the influence of bad weather such as light, haze, sand and dust storm, have become the mainstream selection of car initiative safety, possess huge market demand, and some regional cars have begun to be equipped with like car millimeter wave radar sensor such as anticollision radar, blind area detection radar, and on-vehicle radar detects has become current main research direction.
In the running process of a vehicle, the environment of an actual running road may be complex, various targets with different strengths may exist, for example, a millimeter wave Radar of an automobile is taken as an example, Radar Reflection Cross Sections (RCS) of the various targets may have great differences, the Radar needs to find potential targets timely and early, and perform parameter estimation and tracking on the targets, and detection omission is easily generated for detection of a weak target (i.e., a remote target with a smaller RCS). In order to detect a weak target (i.e. a remote target with a smaller RCS), a method of reducing a detection threshold is usually adopted at present to ensure that the weak target is not leaked, however, since the reduction of the detection threshold brings a large amount of False alarms, generates a large amount of False targets, and seriously affects the actual system operation, when a detection method such as a CFAR (Constant False Alarm probability) is adopted for detection, in order to reduce the False Alarm probability caused by noise, the threshold is generally set to be higher, so that a target signal-to-noise Ratio exceeds 10dB, so that stable detection is possible,
the most widely used waveform system is to transmit a Frequency Modulated Continuous Wave (FMCW) signal with multiple periods, each having a Frequency sweep period TchirpDistance and radial velocity measurements may be made simultaneously for each target in a multi-target scene, the transmitted waveforms being as shown in FIG. 1. The waveform system can measure the distance and the speed of a plurality of targets at the same time, and the automobile millimeter wave radar is a Linear Frequency Modulation (LFM) signal which transmits a plurality of pulses, and the target detection is realized by sampling and signal processing each pulse.
When the vehicle-mounted radar of the automotive millimeter wave radar detects a target, a typical method is to perform sampling and Fourier Transform (FFT) on each pulse to obtain a range-doppler matrix, then perform target detection by using a CFAR detection algorithm, obtain target parameter information including a target distance and a target speed, obtain a detected target result, perform target angle estimation among a plurality of array elements, obtain parameter information above the target, and perform tracking filtering and track management on the target. As shown in fig. 2, specifically, target detection is performed first, whether a target exists is determined, when an exact target is obtained, a target angle estimation module is started to perform angle estimation, after the above information of the target is obtained, the information is input into a tracking filter module and a track management module, and the target information is finally displayed on a terminal and presented to a user.
No matter what kind of target DOA (Degree of Arrival, angle estimation) estimation algorithm (beam scanning FFT method, super resolution algorithm such as music (multiple SIgnal classification) algorithm, etc.) is adopted in the target detection method of the vehicle-mounted radar, since angle estimation must be performed after target detection, detection is performed based on echo amplitude intensity of a target in the current detection, and a higher threshold value is usually set in consideration of the need of avoiding generating too many false targets in CFAR detection, so that some weak targets with small remote RCS cannot be detected in time, for example, a distant human target, resulting in target omission. Therefore, the RCS of targets such as human bodies is weak, so that the remote detection of human bodies becomes an important problem of vehicle-mounted radars.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the technical problems in the prior art, the invention provides the vehicle-mounted radar target detection estimation method and device based on angle estimation, which have the advantages of simple realization method, low cost, good weak target detection performance, low omission factor and low false alarm rate.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
a vehicle-mounted radar target detection and estimation method based on angle estimation comprises the following steps:
s1, angle estimation: obtaining an echo signal of a target radar and carrying out angle estimation to obtain angle spectrum data;
s2, primary target detection: graying the angle spectrum data obtained in the step S1 to preliminarily detect a target higher than a preset threshold value as a real target, so as to obtain grayed angle spectrum data;
s3, target secondary detection: and executing target detection on the grayed angle spectrum data obtained in the step S2 to obtain a final target detection result and output the final target detection result.
Further, in the step S1, an angle estimation algorithm based on angle estimation is specifically adopted to perform angle estimation, so as to obtain the angle spectrum data.
Further, the specific step of performing angle estimation by the angle estimation algorithm based on angle estimation includes:
dividing angles into NsA complete set of discrete grid points, where N iss＝ρsN, and Ns＞＞N，ρsIs a proportionality coefficient, and N is the number of array elements contained in the array; and the echo signal x of the radarcExpressed as space-time steering vectors s (f) corresponding to different grid pointss,i) Multiplied by the corresponding amplitude alpha (n) and superimposed, i.e. the radar signal xcIs represented by the formula:
wherein N is more than or equal to 1 and less than or equal to NsN represents noise;
the radar signal xcThe expression of (a) is converted into a matrix form, and the equation of the matrix form is obtained as:
xc＝Φ(d)αC+n
wherein,
αCis a vector of reflection coefficients
And solving the obtained matrix form equation by using an estimation algorithm to obtain the angle spectrum data of sparse recovery.
Further, when the graying processing is performed in step S2, specifically, the position higher than the preset threshold in the angle spectrum data is set to be 1, and the position lower than the preset threshold in the angle spectrum data is set to be 0.
Further, the preset threshold is obtained by specifically obtaining a maximum amplitude value in the angle spectrum data, and multiplying the obtained maximum amplitude value by a preset scale factor, where the size of the preset scale factor is in a range of 0-1.
Further, the step S2 further includes a step of non-coherent accumulation of multi-frame data, which includes the specific steps of: and adding the multi-frame graying angle spectrum data obtained in a plurality of coherent processing periods to obtain the final graying angle spectrum output.
Further, in the step S3, a fixed threshold target detection method is specifically adopted when target detection is performed.
Further, the threshold used by the target detection method with the fixed threshold is greater than the preset threshold.
An on-vehicle radar target detection estimation device based on angle estimation includes:
the angle estimation module is used for acquiring an echo signal of the target radar and carrying out angle estimation to obtain angle spectrum data;
the target preliminary detection module is used for carrying out gray processing on the angle spectrum data obtained by the angle estimation module so as to preliminarily detect a target higher than a preset threshold value as a real target and obtain gray angle spectrum data;
and the target secondary detection module is used for executing target detection on the grayed angle spectrum data obtained by the target primary detection module to obtain a final target detection result and outputting the final target detection result.
An angle estimation-based vehicle-mounted radar target detection estimation device is characterized by comprising a processor, wherein an executable computer program is stored in the processor, and the processor is configured to execute the method.
Compared with the prior art, the invention has the advantages that:
1. according to the vehicle-mounted radar target detection and estimation method and device based on angle estimation, angle estimation is carried out firstly, the estimated angle spectrogram data is grayed, so that the angle spectrogram data is utilized for carrying out first target detection, and compared with a traditional target detection method based on echo amplitude intensity of a target, all targets including long-distance weak and small targets can be effectively detected, and the condition that the weak and small targets such as a human body are missed to be detected is avoided; meanwhile, the gray scale angle spectrum after primary target detection is subjected to primary target detection again, so that a large number of false alarm targets can be filtered out to finally obtain accurate real targets, the problem of radar on weak and small target detection can be solved, the capability of remotely detecting the weak and small targets is improved, and simultaneously, the introduction of a large number of false alarm targets is avoided.
2. The vehicle-mounted radar target detection and estimation method and device based on angle estimation further perform angle estimation by using an angle estimation method of sparse recovery, can fully play the angle measurement performance of the sparse recovery under the condition of low signal-to-noise ratio, and perform first detection according to the angle spectrum data of the sparse recovery, so that all targets and possible noise target signals can be detected as far as possible, and the target detection performance including weak targets is improved.
3. According to the vehicle-mounted radar target detection and estimation method and device based on angle estimation, multi-frame gray-scale angle spectrum data are added through non-coherent accumulation of multi-frame data, secondary target detection is carried out on the accumulated angle spectrum data, the amplitude of a weak target can be enhanced, meanwhile, the influence of noise is reduced, therefore, false alarm targets generated by the noise can be effectively filtered, and the detection precision is improved.
Drawings
Fig. 1 is a waveform schematic diagram of a classical FMCW transmission sequence.
Fig. 2 is a schematic flow chart of a prior art for realizing target detection by an automotive millimeter wave radar.
Fig. 3 is a schematic flow chart of implementation of the vehicle-mounted radar target detection and estimation method based on angle estimation in the embodiment.
Fig. 4 is a schematic diagram of the principle of acquiring a range-doppler matrix by using a single array element in the present embodiment.
FIG. 5 is a diagram of an angular spectrum with a signal-to-noise ratio of 0dB obtained in an embodiment of the present invention.
Fig. 6 is a schematic diagram of the angular spectra of different CPI data obtained in an embodiment of the present invention.
Fig. 7 is a schematic diagram of a graying processing result obtained in the embodiment of the present invention.
Fig. 8 is a schematic diagram of the principle of implementing the multi CPI graying angle spectrum addition in the embodiment of the present invention.
Fig. 9 is a schematic flow chart of implementing non-coherent accumulation of multi-frame data and target detection and tracking in an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and specific preferred embodiments of the description, without thereby limiting the scope of protection of the invention.
As shown in fig. 3, the steps of the vehicle-mounted radar target detection estimation method based on angle estimation in the present embodiment include:
s1, angle estimation: obtaining an echo signal of a target radar and carrying out angle estimation to obtain angle spectrum data;
s2, primary target detection: graying the angle spectrum data obtained in the step S1 to preliminarily detect a target higher than a preset threshold value as a real target, so as to obtain grayed angle spectrum data;
s3, target secondary detection: and performing target detection on the grayed angle spectrum data obtained in the step S2 to obtain a final target detection result and output the final target detection result.
In the embodiment, angle estimation is performed first, and graying is performed on angle spectrogram data obtained through estimation, so that first target detection is performed by using the angle spectrogram data, because the input quantity during target detection is the angle spectrogram data, and an angle spectrum is a pseudo spectrum, is not necessarily in positive correlation with the strength of a target completely, but can reflect the relative relationship of signals contained therein, compared with a traditional target-based echo amplitude strength target detection method, all possible targets containing long-distance weak and small targets can be effectively detected, and the detection omission of weak and small targets such as a human body is avoided; meanwhile, the angle measurement value of the real target has certain stability, the target angle information generated by noise has randomness, the gray-scale angle spectrum after the primary target detection is subjected to the primary target detection, a large number of false alarm targets in the gray-scale angle spectrum can be filtered, and the accurate real target is finally obtained, so that the problem of radar on the detection of weak and small targets can be solved, the capability of remotely detecting the weak and small targets is improved, and the introduction of a large number of false alarm targets is avoided.
When acquiring an echo signal of a radar, the present embodiment first performs two FFT processes, where the first FFT process corresponds to a distance dimensional echo of each echo pulse to obtain an output corresponding to each range cell, and then performs a second FFT process, i.e., a doppler process, corresponding to a designated range cell to obtain a two-dimensional range-doppler matrix.
Taking a millimeter wave radar as an example, a single array element range-doppler matrix is obtained as shown in fig. 4, that is, the range-doppler matrix is obtained through two FFT processes, the first FFT process is performed on range-dimensional echoes corresponding to each pulse to obtain outputs corresponding to each range cell, and then the second FFT process, that is, the doppler process, is performed, so that a two-dimensional range-doppler matrix can be obtained. The above processing is performed on each array element, and assuming that the number of the receiving array elements is N, N range-doppler matrices can be obtained in total.
After the two-dimensional distance-doppler matrix is obtained, angle estimation is performed, in step S1, angle estimation is performed by using an angle estimation algorithm based on angle estimation, so as to obtain angle spectrum data. The angle estimation method of sparse recovery is insensitive to the target signal-to-noise ratio, and can still accurately measure the angle particularly under the condition of low signal-to-noise ratio, such as about 0dB of the target signal-to-noise ratio. In the embodiment, the angle estimation is performed by using the angle estimation method of sparse recovery, so that the angle measurement performance of the sparse recovery under the condition of low signal to noise ratio can be fully exerted, and the first detection is performed according to the angle spectrum data of the sparse recovery, so that all targets and possible noise target signals can be detected as far as possible, and the target detection performance including weak targets is improved.
In this embodiment, the specific steps of performing angle estimation based on the angle estimation algorithm include:
first divide the angle into NsA complete set of discrete grid points, where N iss＝ρsN, and Ns＞＞N，ρsThe number of the array elements contained in the array is N, namely the resolution of the divided grids is far greater than the resolution of a radar airspace;
the radar echo can be regarded as the superposition of signal echoes from different directions, and the echo signal x of the radar iscExpressed as space-time steering vectors s (f) corresponding to different grid pointss,i) Multiplied by the corresponding amplitude alpha (n) and superimposed, i.e. the radar signal xcIs represented by the formula:
wherein N is more than or equal to 1 and less than or equal to NsN represents noise;
will radar signal xcThe expression of (a) is converted into a matrix form, and the equation of the matrix form is obtained as:
xc＝Φ(d)αC+n (2)
wherein,
αCis a vector of reflection coefficients
And solving the obtained matrix form equation (2) by using an estimation algorithm to obtain angle estimation data of sparse recovery.
For target angle estimation, assuming that individual targets come from some directions, firstly dividing angles according to the above method to form a complete set, regarding radar echoes as superposition of signal echoes from different directions, constructing an expression shown in formula (1), and converting the expression into a matrix form shown in formula (2), wherein the dimension of rows in formula (2) is far lower than that of columns, namely the formula is ill-conditioned, and the problem of formula (2) is that l is the actual problem0The norm problem, which is very difficult to solve due to its high discontinuity, can be converted to l1The norm problem is solved approximately, specifically, the solution can be performed by an lasso (last absolute secret and selection operator) algorithm, that is, a relatively refined model is obtained by constructing a penalty function, so that the model compresses some coefficients, and the coefficients are set to be zero, which can be expressed as:
wherein κLTo adjust the coefficient, σnIs the noise level.
Specifically, a LASSO algorithm is used for solving according to the formula (3), and a final angle estimation result is obtained.
The further optimization problem described above may specifically be solved efficiently using semi-deterministic planning (SDPs).
In a specific application embodiment, the above sparse recovery is sampled to perform angle estimation, and an angle spectrum obtained by using only one snapshot data is shown in fig. 5, where the number of array elements is 16, the target incoming wave direction is 10 °, and the target signal-to-noise ratio is set to 0 dB. As can be seen from fig. 5, the detection includes both the angle value of the real target and the false target output caused by noise.
In this embodiment, when performing the graying processing in step S2, specifically, the position higher than the preset threshold in the angle spectrum data is set to be 1, and the position lower than the preset threshold in the angle spectrum data is set to be 0, that is, the position higher than the preset threshold is determined as the target. Since a large number of values of the angle spectrum obtained by sparse recovery are 0, the threshold setting in the first detection in this embodiment is different from a conventional method such as a Constant False Alarm Rate (CFAR) method, in order to avoid generating a large number of False targets, the preset threshold is specifically obtained by acquiring a maximum amplitude value in the angle spectrum data and multiplying the acquired maximum amplitude value by a preset scale factor, where the size of the preset scale factor is in a range of 0 to 1, for example, the factor may be 0.2 or 0.1, and the preset threshold is specifically set according to an actual situation.
The angle estimation of the real target has stability, while the angle of the target caused by noise has randomness, and in the specific application embodiment, simulation is performed to obtain the angle spectrum of different CPI data as shown in fig. 6, wherein the position above the threshold is set to 1, and the position below the threshold is set to 0, a new vector is obtained, the vector contains a large number of 0, the value of the vector is 1 only at individual positions, and the obtained graying processing result is specifically shown in fig. 7.
In this embodiment, step S2 further includes a step of non-coherent accumulation of multi-frame data, which includes the specific steps of: and adding the multi-frame graying angle spectrum data obtained in a plurality of Coherent Processing periods (CPI) to obtain the final graying angle spectrum output. The sparse recovery angle estimation method is insensitive to a target signal-to-noise ratio, and can still accurately measure an angle when the target signal-to-noise ratio is about 0dB, but possibly, due to some noise signals, false measurement results can be generated, for a real target, the target angle measurement has relative stability, and for measurement results caused by noise, namely, due to the randomness of the noise, angle measurement results for different CPI data have randomness.
Assuming that there are L CPI data, for each CPI, after obtaining an angle-scanned vector by specifically adopting a sparse recovery method, splicing at different times, where each CPI corresponds to one row of a matrix, and adding all the grayed angle spectra of the CPI participating in the joint processing to obtain an added vector shown in fig. 8, as shown in fig. 9, after performing secondary target detection on the added vector, real target information can be obtained by detection, and the detected target is input to a target tracking and track management module for target tracking and track management, and finally the real target information is output to a user.
In this embodiment, a target detection method with a fixed threshold is specifically adopted when target detection is performed in step S3, that is, if the target is higher than the specified fixed threshold, the target is considered to be a real target, and the finally detected target information is transmitted to the back-end module, and if the target is lower than the specified fixed threshold, the target is considered to be a false target, and the false target is removed. The above-mentioned target detection method with fixed threshold may specifically adopt a detection method such as CFAR, and the used threshold may specifically be configured to be larger than a preset threshold in the preliminary detection of the target, so as to remove a false alarm caused by noise therein by using a higher threshold, and meanwhile, since the weak target can be enhanced after the non-coherent accumulation of multi-frame data, the weak target can still be detected. For example, for the automotive millimeter wave radar application, the number of targets at the same distance and the same speed point is usually not too many, and the fixed threshold in the secondary detection may be set to be higher, specifically, the maximum value may be 0.7 or 0.8.
When the radar target detection is realized, in the embodiment, after a range-doppler matrix is obtained from radar echo data, angle estimation is performed by using an estimation method based on the angle estimation, first detection is performed according to sparsely restored angle spectrum data, and a target and possible noise target signals are detected to detect all possible targets including a weak target by fully utilizing the angle measurement performance of sparsely restored angle spectrum data under the condition of low signal-to-noise ratio; and then, graying angle spectrum data is realized according to the detection result, accumulated summation is carried out on the grayed result so as to enhance the accumulated summation of the target by utilizing the stability of the target angle and the randomness of the noise target direction, then, the second detection is carried out to obtain a real target, the detection of a radar long-distance weak target such as a person can be realized, meanwhile, the number of false targets can be greatly reduced, and finally, the low signal-to-noise ratio target detection with low false alarm probability constraint is realized.
The vehicle-mounted radar target detection and estimation device based on angle estimation comprises:
the angle estimation module is used for acquiring an echo signal of the target radar and carrying out angle estimation to obtain angle spectrum data;
the target preliminary detection module is used for carrying out gray processing on the angle spectrum data obtained by the angle estimation module so as to preliminarily detect a target higher than a preset threshold value as a real target and obtain gray angle spectrum data;
and the target secondary detection module is used for executing target detection on the grayed angle spectrum data obtained by the target primary detection module to obtain a final target detection result and outputting the final target detection result.
The angle estimation-based vehicle-mounted radar target detection and estimation device in this embodiment corresponds to the angle estimation-based vehicle-mounted radar target detection and estimation method one to one, which is not described herein in detail.
In another embodiment, the vehicle-mounted radar target detection estimation device based on angle estimation of the present invention may further include: the vehicle-mounted radar target detection estimation method based on the angle estimation comprises a processor, wherein an executable computer program is stored in the processor, and the processor is configured to execute the vehicle-mounted radar target detection estimation method based on the angle estimation.
The foregoing is considered as illustrative of the preferred embodiments of the invention and is not to be construed as limiting the invention in any way. Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical spirit of the present invention should fall within the protection scope of the technical scheme of the present invention, unless the technical spirit of the present invention departs from the content of the technical scheme of the present invention.
Claims (9)
1. A vehicle-mounted radar target detection and estimation method based on angle estimation is characterized by comprising the following steps:
s1, angle estimation: obtaining an echo signal of a target radar and carrying out angle estimation to obtain angle spectrum data;
s2, primary target detection: graying the angle spectrum data obtained in the step S1 to preliminarily detect a target higher than a preset threshold value as a real target, so as to obtain grayed angle spectrum data;
s3, target secondary detection: performing target detection on the grayed angle spectrum data obtained in the step S2 to obtain a final target detection result and outputting the final target detection result;
the step S1 specifically performs angle estimation by an angle estimation method based on sparse recovery, and specifically includes:
dividing angles into NsA complete set of discrete grid points, where N iss＝ρsN, and Ns＞＞N，ρsIs a proportionality coefficient, and N is the number of array elements contained in the array; and the echo signal x of the radarcExpressed as space-time steering vectors s (f) corresponding to different grid pointss,i) Multiplying by corresponding amplitude alpha (n) and superposing;
the radar signal xcConverting the expression of (A) into a matrix form to obtain a matrix form equation;
and solving the obtained matrix form equation by using an estimation algorithm to obtain the angle spectrum data of sparse recovery.
2. The angle estimation-based vehicle-mounted radar target detection estimation method according to claim 1, wherein the radar signal x iscIs represented by the formula:
wherein N is more than or equal to 1 and less than or equal to NsN represents noise;
the radar signal xcThe expression of (a) is converted into a matrix form, and the obtained matrix form equation is:
xc＝Φ(d)αC+n
wherein,
αCis a vector of reflection coefficients
And solving the obtained matrix form equation by using an estimation algorithm to obtain the angle spectrum data of sparse recovery.
3. The method for detecting and estimating the vehicle-mounted radar target based on the angle estimation of claim 1, wherein when performing the graying processing in step S2, a position higher than a preset threshold in the angle spectrum data is specifically set to be 1, and a position lower than the preset threshold is specifically set to be 0.
4. The vehicle-mounted radar target detection and estimation method based on angle estimation as claimed in claim 3, wherein the preset threshold is obtained by specifically obtaining a maximum amplitude value in the angle spectrum data, and multiplying the obtained maximum amplitude value by a preset scaling factor, and the size of the preset scaling factor is within a range of 0 to 1.
5. The vehicle-mounted radar target detection and estimation method based on angle estimation according to any one of claims 1 to 4, characterized in that the step S2 further comprises a multi-frame data non-coherent accumulation step, specifically comprising: and adding the multi-frame graying angle spectrum data obtained in a plurality of coherent processing periods to obtain the final graying angle spectrum output.
6. The vehicle-mounted radar target detection estimation method based on angle estimation according to any one of claims 1 to 4, characterized in that, in the step S3, a target detection method with a fixed threshold is specifically adopted when target detection is performed.
7. The angle estimation-based vehicle-mounted radar target detection estimation method according to claim 6, characterized in that: the threshold used by the target detection method of the fixed threshold is larger than the preset threshold.
8. An on-vehicle radar target detection estimation device based on angle estimation is characterized by comprising:
the angle estimation module is used for acquiring an echo signal of the target radar and carrying out angle estimation to obtain angle spectrum data;
the target preliminary detection module is used for carrying out gray processing on the angle spectrum data obtained by the angle estimation module so as to preliminarily detect a target higher than a preset threshold value as a real target and obtain gray angle spectrum data;
the target secondary detection module is used for executing target detection on the grayed angle spectrum data obtained by the target primary detection module to obtain a final target detection result and outputting the final target detection result;
the angle estimation module specifically performs angle estimation through an angle estimation method based on sparse recovery, and the angle estimation method comprises the following steps:
dividing angles into NsA complete set of discrete grid points, where N iss＝ρsN, and Ns＞＞N，ρsIs a proportionality coefficient, and N is the number of array elements contained in the array; and the echo signal x of the radarcExpressed as space-time steering vectors s (f) corresponding to different grid pointss,i) Multiplying by corresponding amplitude alpha (n) and superposing;
the radar signal xcConverting the expression of (A) into a matrix form to obtain a matrix form equation;
and solving the obtained matrix form equation by using an estimation algorithm to obtain the angle spectrum data of sparse recovery.
9. An angle estimation-based vehicle-mounted radar target detection estimation device, characterized by comprising a processor, wherein an executable computer program is stored in the processor, and wherein the processor is configured to execute the method of any one of claims 1 to 7.
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