CN111983595A - Indoor positioning method and device - Google Patents
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- G—PHYSICS
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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
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- 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
- 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
- G01S13/66—Radar-tracking systems; Analogous systems
- G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
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- G—PHYSICS
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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
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Abstract
The embodiment of the application discloses an indoor positioning method and device. The method comprises the following steps: firstly, demodulating a millimeter wave radar signal to obtain a complex signal; then, carrying out signal processing on the complex signals to obtain measurement point information; next, obtaining track information of at least one detection object according to the measuring point information and the motion model obtained in a period of time; then, a target object and track information of the target object are identified from the at least one detection object according to the Doppler information and the object classification method in the measuring point information. By using the method, the indoor target object can be positioned, tracked and identified by using the millimeter wave radar based on the complex baseband architecture. Therefore, the application and development of the millimeter wave radar in the smart home scene can be further promoted, and the method has great significance.
Description
Technical Field
The present application relates to the field of communications technologies, and in particular, to a method and an apparatus for performing indoor positioning using a communications technology.
Background
With the development of automatic driving in recent years, the millimeter wave radar technology has also been rapidly developed, and especially the emergence of millimeter wave radar SOC chip provides guarantee for the millimeter wave radar to enter large-scale application, and the millimeter wave radar has not only been limited to vehicle-mounted application, but also can be widely applied to the fields such as industry, security protection, smart home.
Especially in the neighborhood of smart homes, the millimeter wave radar can play an important role as a novel sensing device with the advantages of all-weather all-day operation and no relation to personal privacy. The millimeter wave radar can be installed indoors, pedestrian positioning is achieved through sensing of indoor personnel, and the millimeter wave radar can also be used in the intelligent household fields of intelligent power supply energy-saving control, intelligent alarming of external personnel, intelligent air-conditioning air-blowing control according to pedestrian tracks and the like.
However, the existing millimeter wave radar processing method mainly aims at vehicle-mounted application scenes, an effective processing method is lacked for indoor positioning and identification of application scenes, or a processing method based on a real baseband architecture is only realized, the requirement on bandwidth is high, the signal-to-noise ratio is poor, and the application range is limited. Therefore, how to provide a complete detailed processing method of millimeter wave radar based on complex baseband architecture, which can be applied to different millimeter wave radar platforms to realize positioning, tracking and identification of indoor pedestrians becomes a technical problem to be solved for wide application of millimeter wave radar in smart home scenes.
Disclosure of Invention
In order to solve the above problems, embodiments of the present application provide an indoor positioning method and apparatus.
According to a first aspect of embodiments of the present application, an indoor positioning method includes: receiving a millimeter wave radar signal, wherein the millimeter wave radar signal comprises a transmitting signal and an echo signal; demodulating the millimeter wave radar signal to obtain a complex signal; carrying out signal processing on the complex signals to obtain measuring point information, wherein the measuring point information comprises distance information, azimuth information and Doppler information; obtaining track information of at least one detection object according to the measuring point information and the motion model obtained in a period of time; and identifying a target object and track information of the target object from at least one detection object according to Doppler information and an object classification method in the measuring point information.
According to an embodiment of the present application, after identifying the target object and the track information of the target object from the at least one detected object, the method further includes: converting the track information of the target object into application-level data required by upper-layer application; and sending the application level data to an upper layer application.
According to an embodiment of the present application, a method for obtaining measurement point information by performing signal processing on a plurality of signals includes: obtaining distance direction information corresponding to the complex signals according to the frequency of the complex signals and the corresponding relation between the frequency and the distance; obtaining a distance and azimuth two-dimensional thermodynamic diagram according to distance direction information and a high-resolution radar angle measurement method; respectively carrying out distance one-dimensional detection and direction one-dimensional detection on the distance and direction two-dimensional thermodynamic diagram to obtain distance information and direction information; carrying out space domain filtering beam synthesis processing on the distance information and the azimuth information to obtain a beam synthesis signal; and performing Doppler spectrum conversion on the beam forming signals to obtain Doppler information.
According to an embodiment of the application, a distance and azimuth two-dimensional thermodynamic diagram is obtained according to distance direction information and a high-resolution radar angle measurement method, and the method comprises the following steps: static clutter elimination processing is carried out on distance direction information with the same distance but different transmitting units to obtain a first processing result; acquiring the inverse covariance matrix of first processing results with the same distance but different transmitting units; and forming a distance and orientation two-dimensional thermodynamic diagram in an azimuth enumeration stepping mode according to the inverse covariance matrix and a Capon spectrum estimation algorithm.
According to an embodiment of the present application, performing doppler spectrum conversion on a beam-formed signal to obtain doppler information includes: performing linear frequency modulation signal fast Fourier transform on the beam synthesis signal to obtain a Doppler spectrum; performing modulo calculation on the Doppler spectrum to obtain Doppler spectrum information; and performing Doppler one-dimensional detection on the Doppler frequency spectrum information to obtain Doppler information.
According to an embodiment of the present application, obtaining track information of at least one detection object according to measurement point information and a motion model obtained within a period of time, including determining whether the measurement point information meets a new track starting condition, if yes, entering a detection mode and further detecting whether a frame number of a measurement point continuously hitting a predicted track exceeds a first threshold, if yes, entering an activation mode and executing the following method: predicting track information according to the measuring point information and the motion model; and allocating the subsequent measuring point information to the track information to obtain the track information of at least one detection object.
According to an embodiment of the present application, in a process of allocating subsequent measurement point information to track information, the method further includes: and if the number of the frames of the continuous non-measured points which can be allocated to the track information exceeds a second threshold value, entering a loss mode and finishing the operation.
According to an embodiment of the present application, allocating subsequent measurement point information to track information to obtain track information of at least one detection object includes: and detecting whether the subsequent measuring point information meets a first condition, if so, distributing the corresponding measuring point information to the track information, if not, further detecting whether the corresponding measuring point meets a second condition, and if so, acquiring the track information corresponding to the corresponding measuring point.
According to an embodiment of the present application, identifying a target object and track information of the target object from at least one detection object according to doppler information and an object classification method in measurement point information includes: extracting micro Doppler characteristics of at least one detection object according to Doppler information in the measuring point information, wherein the micro Doppler characteristics comprise micro Doppler shift, trunk micro Doppler bandwidth and integral average micro Doppler center; and identifying a target object and track information of the target object from at least one detection object by adopting a nearest neighbor classification method and micro Doppler characteristics.
According to a second aspect of embodiments of the present application, an indoor positioning device includes: the signal receiving module is used for receiving millimeter wave radar signals, wherein the millimeter wave radar signals comprise transmitting signals and echo signals; the signal demodulation module is used for demodulating the millimeter wave radar signal to obtain a complex signal; the signal processing module is used for carrying out signal processing on the complex signals to obtain measuring point information, and the measuring point information comprises distance information, azimuth information and Doppler information; the flight path acquisition module is used for acquiring flight path information of at least one detection object according to the measuring point information and the motion model acquired within a period of time; and the target track determining module is used for identifying a target object and track information of the target object from at least one detection object according to Doppler information and an object classification method in the measuring point information.
The embodiment of the application provides an indoor positioning method and device. The method comprises the following steps: firstly, demodulating a millimeter wave radar signal to obtain a complex signal; then, carrying out signal processing on the complex signals to obtain measurement point information; next, obtaining track information of at least one detection object according to the measuring point information and the motion model obtained in a period of time; then, a target object and track information of the target object are identified from the at least one detection object according to the Doppler information and the object classification method in the measuring point information. By using the method, the indoor target object can be positioned, tracked and identified by using the millimeter wave radar based on the complex baseband architecture. Therefore, the application and development of the millimeter wave radar in the smart home scene can be further promoted, and the method has great significance.
It is to be understood that the teachings of this application need not achieve all of the above-described benefits, but rather that specific embodiments may achieve specific technical results, and that other embodiments of this application may achieve benefits not mentioned above.
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The above and other objects, features and advantages of exemplary embodiments of the present application will become readily apparent from the following detailed description read in conjunction with the accompanying drawings. Several embodiments of the present application are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which:
in the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.
Fig. 1 is a schematic flow chart illustrating an implementation of an indoor positioning method according to an embodiment of the present application;
fig. 2 is a schematic diagram illustrating a specific implementation flow of an indoor positioning method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an indoor positioning apparatus according to an embodiment of the present application.
Detailed Description
In order to make the objects, features and advantages of the present application more obvious and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only a part of the embodiments of the present application, and not all 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 application.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
According to a first aspect of embodiments of the present application, an indoor positioning method, as shown in fig. 1, includes: an operation 110 of receiving millimeter wave radar signals, wherein the millimeter wave radar signals include a transmit signal and an echo signal; operation 120, demodulating the millimeter wave radar signal to obtain a complex signal; in operation 130, performing signal processing on the plurality of signals to obtain measurement point information, where the measurement point information includes distance information, direction information, and doppler information; operation 140, obtaining track information of at least one detection object according to the measurement point information and the motion model obtained within a period of time; in operation 150, a target object and track information of the target object are identified from the at least one detection object according to the doppler information and the object classification method in the measurement point information.
In operation 110, the millimeter wave radar is a radar operating in the millimeter wave band (millimeter wave) for detection. Generally, the millimeter wave radar signal refers to a signal in a frequency domain (with a wavelength of 1-10 mm) of 30-300 GHz. The millimeter wave radar has the characteristics of high spatial resolution, strong penetrating power, interference resistance and the like, can distinguish and identify very small objects, and can simultaneously identify a plurality of objects. The received millimeter wave radar signal includes a transmission signal transmitted by the millimeter wave radar and used for identifying the object, and also includes a signal with attenuation delay generated by the reflection of the transmission signal by a reflector and the absorption of a part of energy by the reflector.
In addition, it should be noted that, since the method for indoor positioning in the embodiment of the present application is based on a millimeter wave radar with a complex baseband architecture, the millimeter wave radar here is a millimeter wave radar with complex baseband demodulation capability.
At present, the mainstream millimeter wave radar processing platform mainly comprises two forms: a radio frequency unit and a processing unit are integrated together to form an SOC chip; and the other is that the radio frequency unit and the processing unit are respectively independent chips. The embodiment of the application does not limit the form of the millimeter wave radar processing platform.
In operation 120, demodulation is a process of obtaining information carried by the millimeter-wave radar signal, and typically includes mixing, filtering, and quadrature sampling. Here, the result of the demodulation is a complex signal, i.e., a complex representation of the millimeter wave radar signal. Since the real signal has a spectrum with conjugate symmetry, its negative spectrum part is redundant. The complex signal will remove the negative spectrum part of the real signal, and only the signal of the positive spectrum part is retained, and its real part and imaginary part respectively represent the quadrature information. For digital communications, the complex signal can reduce the effective bandwidth by half, and correspondingly, can reduce the AD sampling rate by half; in addition, since the noise is half of that of real baseband demodulation, the signal-to-noise ratio (SNR) can be improved by 3dB using complex baseband demodulation; moreover, the image frequency interference is eliminated, and the interference elimination of multipath and the like in the indoor positioning signal processing can be guaranteed. Since the embodiment of the present application is implemented based on a complex baseband architecture, and a complex signal is obtained by demodulation, the same advantageous effects can be obtained.
In operation 130, in order to acquire measurement point information including distance information, azimuth information, and doppler information from the complex signals, various signal processing is generally required, for example, denoising various intermediate results and various conversions of signals for acquiring distance information, azimuth information, and doppler information, and the like. The distance information, the direction information and the Doppler information are important data bases for follow-up detection object tracking and detection object identification.
In operation 140, the motion model is a prediction model for predicting the track information of the object corresponding to the measuring point according to the measuring point information, and is generally established according to the direction, speed and general motion rule of the detected object. The track information mainly refers to relevant information for describing a motion trajectory of the detection object over a period of time, for example, a series of position information with time sequence.
Since the millimeter wave radar can simultaneously identify a plurality of objects, when a plurality of detection objects exist in the detection range of the millimeter wave radar, such as an electric fan, a window shade swinging with wind, a pet, etc., the echo signals received in the detection operation 110 are reflected by the plurality of detection objects. Accordingly, the complex signal obtained by demodulating the millimeter wave radar signal in operation 120 and the measurement point information obtained by signal processing the complex signal in operation 130 both include information corresponding to a plurality of detection targets. Therefore, the track information obtained in operation 140 may also be the track information of a plurality of detection objects.
Generally, in the actual application process, the track information of all the detection objects is not needed, but only the track information of a part of the detection objects needed by the application is needed, and the part of the detection objects is the target object. The track information obtained in operation 140 is typically the entire track information of all the detected objects. To acquire the track information of the target object, first, the target object is identified from a plurality of detection objects. In the method for indoor positioning in the embodiment of the application, the detection objects are classified mainly through doppler information in the measurement point information, so that the target object is identified.
Therefore, the indoor positioning method can be used for positioning, tracking and identifying the indoor target object by using the millimeter wave radar based on the complex baseband architecture, so that the application and development of the millimeter wave radar in the intelligent home scene can be further promoted.
According to an embodiment of the present application, after identifying the target object and the track information of the target object from the at least one detected object, the method further includes: converting the track information of the target object into application-level data required by upper-layer application; and sending the application level data to an upper layer application.
The track information of the target object, typically obtained in operation 150, is served for various specific applications. For example, indoor pedestrian tracks are provided for smart home applications so that the smart home provides corresponding services. Each smart home typically provides data definition and data interface when receiving track information. In this embodiment, the track information of the target object is converted according to the data definitions and data interfaces provided by the applications and is sent to the upper layer application.
According to an embodiment of the present application, a method for obtaining measurement point information by performing signal processing on a plurality of signals includes: obtaining distance direction information corresponding to the complex signals according to the frequency of the complex signals and the corresponding relation between the frequency and the distance; obtaining a distance and azimuth two-dimensional thermodynamic diagram according to distance direction information and a high-resolution radar angle measurement method; respectively carrying out distance one-dimensional detection and direction one-dimensional detection on the distance and direction two-dimensional thermodynamic diagram to obtain distance information and direction information; carrying out space domain filtering beam synthesis processing on the distance information and the azimuth information to obtain a beam synthesis signal; and performing Doppler spectrum conversion on the beam forming signals to obtain Doppler information.
Generally, if there is only one radar, the range direction information can be obtained only by the frequency of the demodulated digital signal, for example, the frequency of the complex signal mentioned in the present application, and the correspondence between the frequency and the range. Wherein, the corresponding relation between the frequency and the distance can be obtained from a comparison table of the signal frequency and the distance. The distance information mainly refers to information in the radar image along the radar wave transmitting direction.
In order to further acquire azimuth information, in the present embodiment, a high-resolution radar angle measurement method is used to convert range information into a range and azimuth two-dimensional thermodynamic diagram, and then further perform one-dimensional detection of azimuth and range directions to extract specific azimuth information and range information, where a common one-dimensional detection method includes a CFAR detection method and the like. After the beam forming signals are obtained according to the distance information and the azimuth information, the corresponding Doppler information can be obtained by performing Doppler spectrum conversion on the beam forming signals.
According to an embodiment of the application, a distance and azimuth two-dimensional thermodynamic diagram is obtained according to distance direction information and a high-resolution radar angle measurement method, and the method comprises the following steps: static clutter elimination processing is carried out on distance direction information with the same distance but different transmitting units to obtain a first processing result; acquiring the inverse covariance matrix of first processing results with the same distance but different transmitting units; and forming a distance and orientation two-dimensional thermodynamic diagram in an azimuth enumeration stepping mode according to the inverse covariance matrix and a Capon spectrum estimation algorithm.
The static clutter is eliminated, the influence on subsequent processing can be reduced, and the accuracy is higher. The Capon spectrum estimation algorithm is an estimation method for realizing the inverse R-1L of the autocorrelation matrix of the observed signal based on a recursive least square method, and has the advantages of low calculation complexity and high convergence speed.
According to an embodiment of the present application, performing doppler spectrum conversion on a beam-formed signal to obtain doppler information includes: performing linear frequency modulation signal fast Fourier transform on the beam synthesis signal to obtain a Doppler spectrum; performing modulo calculation on the Doppler spectrum to obtain Doppler spectrum information; and performing Doppler one-dimensional detection on the Doppler frequency spectrum information to obtain Doppler information.
Among them, Doppler Spectrum (Doppler Spectrum) is a method for characterizing frequency dispersion. Chirp modulation (LFM) is a spread spectrum modulation technique that does not require a pseudo-random code sequence. Because the frequency bandwidth occupied by the chirp signal is much larger than the information bandwidth, a large system processing gain can be obtained. Chirp signals are also called Chirp signals because their spectral bandwidth falls within the audible range, and they are heard as if they are a bird. In the radar positioning technology, the technology can increase the radio frequency pulse width, improve the average transmitting power, enlarge the communication distance and simultaneously maintain enough signal spectrum width without reducing the range resolution of the radar. The doppler one-dimensional detection method includes a CFAR detection method.
According to an embodiment of the present application, obtaining track information of at least one detection object according to measurement point information and a motion model obtained within a period of time, including determining whether the measurement point information meets a new track starting condition, if yes, entering a detection mode and further detecting whether a frame number of a measurement point continuously hitting a predicted track exceeds a first threshold, if yes, entering an activation mode and executing the following method: predicting track information according to the measuring point information and the motion model; and allocating the subsequent measuring point information to the track information to obtain the track information of at least one detection object.
In the embodiment of the application, besides the positioning of the object, the object can be tracked, and the main way of tracking is to acquire the track information of the object. The method for acquiring the flight path is that at least one flight path is predicted according to measuring points acquired within a period of time, and then whether the subsequent measuring points can be distributed to a certain flight path or not is detected. Wherein, the new track starting condition mainly comprises: whether the measurement point is within a certain speed range or distance range; how much information is in the measurement spot and whether the signal-to-noise ratio is high enough.
In the process of acquiring the object track information, the following working modes can be provided: a detection mode, an active mode, and a loss mode. And the detection mode is mainly used for acquiring the information of the measuring points, detecting whether the measuring points hit the predicted flight path or not, namely, whether the measuring points accord with or are close to points in the predicted flight path, and then selecting to enter an activation mode or a discarding mode according to the condition that the measuring points hit the flight path information in a connection mode. When the frame number of the continuous hit predicted track exceeds a first threshold, the track is the real effective track. The value of the first threshold is usually preset based on empirical values. Loss mode, which means that no or particularly few points can be assigned to the trajectory over a period of time, indicates that the tracked object has stopped moving or has disappeared within the range of radar detection, so that the acquisition of the trajectory can be ended as soon as this mode is entered.
According to an embodiment of the present application, in a process of allocating subsequent measurement point information to track information, the method further includes: and if the number of the frames of the continuous non-measured points which can be allocated to the track information exceeds a second threshold value, entering a loss mode and finishing the operation.
Wherein the second threshold is preset based on empirical values. If no measuring points can be allocated to the flight path information, it indicates that the flight path is basically finished, or the measuring points exceed the detection range, so that the operation can enter a discarding mode and finish the operation.
According to an embodiment of the present application, allocating subsequent measurement point information to track information to obtain track information of at least one detection object includes: and detecting whether the subsequent measuring point information meets a first condition, if so, distributing the corresponding measuring point information to the track information, if not, further detecting whether the corresponding measuring point meets a second condition, and if so, acquiring the track information corresponding to the corresponding measuring point.
Here, the first condition is usually that the approach of each measurement point to each trace exceeds a threshold value set according to a previous empirical value, and then the trace is assigned. If the first condition is not met, it is stated that these points may not be points on the existing predicted trajectory. At this time, it is possible to detect whether the measurement point meets the second condition. Here, the second condition mainly refers to whether the measurement point may belong to an undetected trajectory. For example, all the non-compliant points are clustered, and then whether the clusters conform to the new track starting condition is judged, if yes, the track information corresponding to the corresponding measuring points is obtained.
According to an embodiment of the present application, identifying a target object and track information of the target object from at least one detection object according to doppler information and an object classification method in measurement point information includes: extracting micro Doppler characteristics of at least one detection object according to Doppler information in the measuring point information, wherein the micro Doppler characteristics comprise micro Doppler shift, trunk micro Doppler bandwidth and integral average micro Doppler center; and identifying a target object and track information of the target object from at least one detection object by adopting a nearest neighbor classification method and micro Doppler characteristics.
Since many disturbance objects such as electric fans, curtains which are swung by wind, pets, etc. are included in a room, the disturbance is difficult to be eliminated by the previous method. The present embodiment eliminates interference by identifying different targets using different micro-doppler signatures of different objects. Although there are many machine learning recognition methods, these methods are too computationally intensive to be used in smart homes, which are simple, low cost sensors. Therefore, the embodiment of the application adopts the off-line collection of the micro Doppler features of different interferences of a typical scene, and generates the templates of different objects according to the micro Doppler features. The inventor finds that the three characteristics of micro Doppler shift, trunk micro Doppler bandwidth and overall average micro Doppler center can achieve better effect in the aspect of distinguishing interference and pedestrian targets through theoretical analysis and actual tests.
When the classification is carried out according to the nearest neighbor classification method, the classification of the detection object can be obtained by matching the templates through the micro Doppler features of the detection object, and then the target object which is really concerned is identified.
The method is a classification method which finds better performance after testing a certain data volume sample, and the calculation amount is lower, thus being convenient to realize.
How the method for implementing an indoor positioning in an application according to the embodiment of the present application is described in detail below with reference to fig. 2.
Fig. 2 shows a specific implementation process of an indoor positioning method according to an embodiment of the present invention, which is implemented based on a complex baseband architecture system. In particular to this application, the complex baseband architecture includes an antenna component, a radio frequency component, and a processing unit component. The antenna component is used for realizing the transmission and the reception of signals; the radio frequency component is used for realizing local oscillation signal generation, transmission signal control and complex baseband demodulation; and the processing unit component is used for realizing signal processing, multi-target tracking, target identification and high-level application logic of the millimeter wave radar.
Fig. 2 shows a specific implementation process of an indoor positioning method according to an embodiment of the present application, which mainly includes the following steps:
And step 202, filtering the mixed signal by using a low-pass filter and a high-pass filter to obtain an intermediate frequency signal.
The low-pass filter filters the high-frequency component after frequency mixing and the long-distance echo signal, and the cut-off frequency of the low-pass filter determines the farthest detection distance of the millimeter wave radar in a non-fuzzy state; the high-pass filter mainly filters out direct-current components, and the cut-off frequency of the high-pass filter determines the nearest detection distance of the millimeter-wave radar.
And 204, performing one-dimensional fast Fourier transform on the demodulated complex signal according to a chirp period of the chirp continuous wave to obtain frequency information of the complex signal, and then obtaining corresponding distance direction information according to a corresponding relation between the distance and the frequency to complete distance measurement.
And step 205, obtaining a distance and direction two-dimensional thermodynamic diagram by a Capon high-resolution angle measurement method on the basis of the distance direction information. The method specifically comprises the following steps: (1) and (4) eliminating static clutter. Averaging signals among different pulses (chirp) of the same distance unit of each antenna to be used as static information, and subtracting the static information from the information by using the distance to eliminate the static clutter; (2) covariance matrix inversion: obtaining a covariance matrix by solving cross correlation of signals among different pulses (chirp) of the same distance unit of each antenna for eliminating the static clutter, and further obtaining covariance matrix inversion by an inversion method; (3) according to a Capon spectrum estimation algorithm, the azimuth angle forms a distance and azimuth diagram in an enumeration stepping mode.
And step 206, aiming at the distance and orientation thermodynamic diagram obtained in the step 205, performing one-dimensional CFAR detection in the distance direction by using a CASO-CFAR method, preliminarily acquiring distance information of the detection object, and performing CFAR detection in the orientation direction to obtain orientation information of the detection object.
In the detection process, the detection boundary is processed in a circular folding mode, namely for a left boundary point, a left reference point and a protection unit adopt a right boundary point; for the right boundary point, the right reference point and the protection unit thereof adopt the left boundary point.
The step can provide high signal-to-noise ratio for the subsequent Doppler measurement, improve the Doppler measurement precision, and simultaneously suppress side lobes and reduce the interference of the side lobes.
After the processing, detection objects with the same space position but different Doppler can be distinguished. And finally, outputting the distance, the azimuth angle and the Doppler information of the detected object as three-dimensional point cloud information according to the conversion relation, and finishing millimeter wave radar signal processing.
And step 209, predicting the flight path information of the flight path entering the activation mode according to the motion model and the motion state value of the current flight path in the prediction process through the prediction process of the extended Kalman filter. For example, the state vector and covariance for the next time instant are estimated based on the state vector and process covariance matrix for the current time instant.
In step 210, the allocation step includes two parts, namely a threshold function and a scoring function, and the purpose is to enable each flight path to detect whether each measurement point is close enough to the flight path, and to score the measurement point close enough to the flight path. And these measurement points will be assigned to the highest scoring track. For each given track, a threshold function is set with respect to the predicted objective. This threshold function should take into account target maneuvering, target dispersion, and measurement noise, among other factors. An ellipsoid relating to the centroid of the group tracking cluster is constructed in a three-dimensional measurement space using the group residual covariance matrix, and represents a threshold function for defining the respective measurement points observed at the current time. And calculating a normalized distance function as a cost function for all the measuring points in the threshold function, and associating the measuring points with the track information through the cost function. The allocation process is to allocate the measurement points to the flight path with the minimum cost function. Thus, a set of measurement points associated with each track is obtained.
And step 211, clustering the measuring points which are not allocated to any flight path information. And if the target can be clustered into one target and the new track starting condition is met, executing the track acquiring process again to acquire the track information corresponding to the measuring point.
First, a pilot measurement point is selected and a centroid appropriate to it is set. One candidate point at a time is checked for being within the velocity range and within the distance range, and if passing, the centroid (qualified point mean) is recalculated and added to the point cloud. After the above process is completed, the point number and the signal-to-noise ratio of the point cloud are detected. Point clouds will be ignored if the number of points is low or the signal-to-noise ratio is not high enough. If the test is passed, a new flight path is allocated to the measuring point, and meanwhile, a detection mode is entered.
And step 212, updating the state value of the detection object and the innovation covariance matrix by using an extended Kalman method and using the measurement point to obtain the target state at the current moment. For a track target in an active mode, the following three situations may occur in the updating process to cause tracking loss: (1) if the target is static or moves very slowly to be close to a static state, supposing that the target is used as static clutter to be filtered; (2) if the target is not still but not detected, the target is considered to leave the observation area; (3) if the target is not detected in other situations, the target is presumed to be occluded by other objects.
For the scenario of (1), when the target is within the tracking boundary, a large static-to-loss threshold value can be used to prolong the observation time, and the state quantity of the previous moment is reserved in the prediction of the next moment; for the scenarios (2) and (3), if the number of continuously lost frames exceeds the activation loss threshold, deleting the track; if the measuring point exceeds the flight path boundary, the flight path is deleted quickly.
And step 215, classifying and identifying the target by using the extracted target micro-Doppler characteristic and adopting a nearest neighbor algorithm classification method to obtain a target object and track information of the target object.
And step 216, finishing corresponding high-level logic judgment by utilizing the tracking and identifying results and combining with actual application requirements. For example, outputting the judgment result according to the protocol format required by the other party so that the intelligent control terminal can make a reasonable decision, includes: the method is applied to controlling the blowing direction of the air conditioner according to the motion track of the pedestrian, intelligently turning off a main power switch according to whether the pedestrian exists indoors, giving an alarm according to whether the pedestrian enters a monitoring area, and the like.
It should be noted that the specific implementation flow of an application indoor positioning method in the embodiment of the present application shown in fig. 2 is only an exemplary illustration of the embodiment of the present application, and is not a limitation to the implementation manner. The implementer may opt to employ any suitable implementation depending on the specific implementation conditions.
According to a second aspect of the embodiments of the present application, an indoor positioning apparatus 30, as shown in fig. 3, includes: the signal receiving module 301 is configured to receive a millimeter wave radar signal, where the millimeter wave radar signal includes a transmission signal and an echo signal; the signal demodulation module 302 is configured to demodulate the millimeter wave radar signal to obtain a complex signal; the signal processing module 303 is configured to perform signal processing on the complex signal to obtain measurement point information, where the measurement point information includes distance information, direction information, and doppler information; a track acquiring module 304, configured to obtain track information of at least one detection object according to the measurement point information and the motion model obtained within a period of time; and a target track determining module 305, configured to identify a target object and track information of the target object from the at least one detected object according to the doppler information in the measurement point information and an object classification method.
According to an embodiment of the present application, the apparatus 30 further includes: the application level data conversion module is used for converting the track information of the target object into application level data required by upper-layer application; and the application-level data sending unit is used for sending the application-level data to the upper layer application.
According to an embodiment of the present application, the signal processing module 303 includes: the distance direction information acquisition submodule is used for acquiring distance direction information corresponding to the complex signals according to the frequency of the complex signals and the corresponding relation between the frequency and the distance; the distance and direction two-dimensional thermodynamic diagram acquisition submodule is used for acquiring a distance and direction two-dimensional thermodynamic diagram according to distance direction information and a high-resolution radar angle measurement method; the distance information and direction information acquisition submodule respectively performs distance one-dimensional detection and direction one-dimensional detection on the distance and direction two-dimensional thermodynamic diagram to obtain distance information and direction information; the beam synthesis submodule is used for carrying out space-domain filtering beam synthesis processing on the distance information and the azimuth information to obtain a beam synthesis signal; and the Doppler information acquisition sub-module is used for performing Doppler spectrum conversion on the beam forming signals to obtain Doppler information.
According to an embodiment of the present application, the distance and orientation two-dimensional thermodynamic diagram acquisition sub-module includes: the static clutter elimination unit is used for carrying out static clutter elimination processing on the distance direction information with the same distance but different transmitting units to obtain a first processing result; the inverse covariance matrix acquisition unit is used for acquiring inverse covariance matrices of first processing results which have the same distance but different transmitting units; and the distance and orientation two-dimensional thermodynamic diagram acquisition unit is used for forming a distance and orientation two-dimensional thermodynamic diagram in an azimuth enumeration stepping mode according to the inverse covariance matrix and a Capon spectrum estimation algorithm.
According to an embodiment of the present application, the beamforming sub-module includes: the Doppler spectrum acquisition unit is used for carrying out linear frequency modulation signal fast Fourier transform on the beam forming signal to obtain a Doppler spectrum; a Doppler spectrum information obtaining unit, configured to perform modulo operation on a Doppler spectrum to obtain Doppler spectrum information; and the Doppler information acquisition unit is used for performing Doppler one-dimensional detection on the Doppler spectrum information to obtain Doppler information.
According to an embodiment of the present application, a track information acquiring module includes: the new flight path starting condition judgment submodule is used for judging whether the measuring point information meets the new flight path starting condition or not; the activation condition detection submodule is used for further detecting whether the frame number of the continuous hit predicted flight path of the measuring points exceeds a first threshold value; an active mode sub-module for entering an active mode and performing the method of: predicting track information according to the measuring point information and the motion model; and the track distribution submodule is used for distributing the subsequent measuring point information to the track information to obtain the track information of at least one detection object.
According to an embodiment of the present application, the track information obtaining module 304 includes: and the loss condition detection submodule is used for entering a loss mode and finishing the operation if the number of frames of the continuous non-measured points which can be allocated to the track information exceeds a second threshold value.
According to an embodiment of the present application, the track assignment sub-module includes: the first condition detection unit is used for detecting whether the subsequent measurement point information meets a first condition or not; the flight path distribution unit is used for distributing corresponding measuring point information to the flight path information; the second condition detection unit is used for detecting whether the corresponding measuring points meet a second condition or not; and the new track acquiring unit is used for acquiring the track information corresponding to the corresponding measuring points.
According to an embodiment of the present application, the target track information determining module 305 includes: the micro Doppler feature extraction submodule is used for extracting the micro Doppler features of at least one detection object according to the Doppler information in the measuring point information, and the micro Doppler features comprise micro Doppler shift, trunk micro Doppler bandwidth and an integral average micro Doppler center; and the target track information determining submodule is used for identifying the target object and the track information of the target object from the at least one detection object by adopting a nearest neighbor classification method and micro Doppler characteristics.
Here, it should be noted that: the above description of the embodiment of the indoor positioning apparatus is similar to the description of the embodiment of the method, and has similar beneficial effects to the embodiment of the method, and therefore, the description is not repeated. For technical details that have not been disclosed in the description of the embodiments of the indoor positioning apparatus, please refer to the description of the embodiments of the method described above in the present application for understanding, and therefore, for brevity, will not be described again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of a unit is only one logical function division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another device, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
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; can be located in one place or 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, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media capable of storing program codes, such as a removable storage medium, a Read Only Memory (ROM), a magnetic disk, and an optical disk.
Alternatively, the integrated units described above in the present application may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or portions thereof that contribute to the prior art may be embodied in the form of a software product stored in a storage medium, and including several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a removable storage medium, a ROM, a magnetic disk, an optical disk, or the like, which can store the program code.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. An indoor positioning method, characterized in that the method comprises:
receiving millimeter wave radar signals, wherein the millimeter wave radar signals comprise emission signals and echo signals;
demodulating the millimeter wave radar signal to obtain a complex signal;
performing signal processing on the plurality of signals to obtain measuring point information, wherein the measuring point information comprises distance information, azimuth information and Doppler information;
obtaining track information of at least one detection object according to the measuring point information and the motion model obtained in a period of time;
and identifying a target object and track information of the target object from the at least one detection object according to Doppler information and an object classification method in the measuring point information.
2. The method of claim 1, wherein after said identifying a target object from said at least one detected object and track information for said target object, said method further comprises:
converting the track information of the target object into application level data required by upper-layer application;
and sending the application level data to the upper layer application.
3. The method of claim 1, wherein the signal processing the complex signal to obtain measurement point information comprises:
obtaining distance direction information corresponding to the complex signals according to the frequency of the complex signals and the corresponding relation between the frequency and the distance;
obtaining a distance and azimuth two-dimensional thermodynamic diagram according to the distance direction information and a high-resolution radar angle measurement method;
respectively carrying out distance one-dimensional detection and direction one-dimensional detection on the distance and direction two-dimensional thermodynamic diagram to obtain distance information and direction information;
carrying out space domain filtering beam synthesis processing on the distance information and the azimuth information to obtain beam synthesis signals;
and performing Doppler spectrum conversion on the beam forming signals to obtain Doppler information.
4. The method of claim 3, wherein obtaining a range-azimuth two-dimensional thermodynamic diagram from the range-direction information and a high-resolution radar angle measurement method comprises:
static clutter elimination processing is carried out on distance direction information with the same distance but different transmitting units to obtain a first processing result;
acquiring the inverse covariance matrix of first processing results with the same distance but different transmitting units;
and forming a distance and orientation two-dimensional thermodynamic diagram in an azimuth enumeration stepping mode according to the inverse covariance matrix and a Capon spectrum estimation algorithm.
5. The method of claim 3, wherein the Doppler spectrum transforming the beamformed signal to obtain Doppler information comprises:
performing linear frequency modulation signal fast Fourier transform on the beam forming signal to obtain a Doppler spectrum;
performing modulo operation on the Doppler spectrum to obtain Doppler spectrum information;
and performing Doppler one-dimensional detection on the Doppler frequency spectrum information to obtain Doppler information.
6. The method of claim 1, wherein the obtaining of the track information of at least one object to be detected according to the measurement point information and the motion model obtained within a period of time includes determining whether the measurement point information satisfies a new track starting condition, if so, entering a detection mode and further detecting whether a number of frames of the measurement point continuously hitting a predicted track exceeds a first threshold, and if so, entering an activation mode and executing the following method:
predicting track information according to the measuring point information and the motion model;
and allocating the subsequent measuring point information to the track information to obtain the track information of the at least one detection object.
7. The method of claim 6, wherein in the assigning subsequent measurement point information to the track information, the method further comprises:
and if the number of the frames of the continuous non-measured points which can be distributed to the track information exceeds a second threshold value, entering a loss mode and finishing the operation.
8. The method of claim 6, wherein the assigning subsequent measurement point information to the track information to obtain track information of the at least one test object comprises:
and detecting whether the subsequent measuring point information meets a first condition, if so, determining the track information corresponding to the corresponding measuring point from the track information, if not, further detecting whether the corresponding measuring point meets a second condition, and if so, acquiring the track information corresponding to the corresponding measuring point.
9. The method of claim 1, wherein the identifying a target object and track information of the target object from the at least one detection object according to doppler information in the measurement point information and an object classification method comprises:
extracting a micro Doppler feature of the at least one detection object according to Doppler information in the measuring point information, wherein the micro Doppler feature comprises micro Doppler shift, trunk micro Doppler bandwidth and an overall average micro Doppler center;
and identifying a target object and track information of the target object from the at least one detection object by adopting a nearest neighbor classification method and the micro Doppler feature.
10. An indoor positioning device, comprising:
the signal receiving module is used for receiving millimeter wave radar signals, wherein the millimeter wave radar signals comprise transmitting signals and echo signals;
the signal demodulation module is used for demodulating the millimeter wave radar signal to obtain a complex signal;
the signal processing module is used for carrying out signal processing on the complex signals to obtain measuring point information, and the measuring point information comprises distance information, azimuth information and Doppler information;
the flight path information acquisition module is used for acquiring flight path information of at least one detection object according to the measuring point information and the motion model acquired within a period of time;
and the target track information determining module is used for identifying a target object and track information of the target object from the at least one detection object according to Doppler information and an object classification method in the measuring point information.
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