CN115436930A - Method for improving maximum sensing speed of high-resolution millimeter wave radar - Google Patents
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Abstract
The invention discloses a method for improving the maximum sensing speed of a high-resolution millimeter wave radar. And designing a signal processing system, processing the signals received by the millimeter wave radar frame by frame to obtain a point cloud data set, and clustering the point cloud data set respectively to obtain a point cloud clustering data set. And then, according to the point cloud clustering data set, acquiring a point cloud matching data set containing successful matching and unsuccessful matching by utilizing BEV information, selecting an error matrix scheme, and calculating and acquiring a solution fuzzy strategy with the minimum error. And finally, according to a fuzzy solving strategy with the minimum error, carrying out fuzzy solving operation on the point cloud matching data set, and drawing a fuzzy solving radar point cloud result on the optical photo. The invention solves the problem that the high-resolution millimeter wave radar for assisting intelligent driving is limited by spatial resolution and speed resolution, and realizes a wider sensing speed detection range.
Description
Technical Field
The invention belongs to the field of millimeter wave radar measurement, and particularly relates to a method for improving the maximum sensing speed of a high-resolution millimeter wave radar.
Background
The millimeter wave radar has the advantages that the laser radar does not have under the special meteorological condition, and according to the wave propagation theory, the higher the frequency is, the shorter the wavelength is, the higher the resolution ratio is, and the stronger the penetration capability is. Compared with other microwaves, the millimeter waves are high in resolution ratio, good in directivity, strong in anti-interference capability and good in detection performance, and especially in the research field of intelligent driving, and compared with the traditional laser radar, the target recognition, analysis, perception and information transmission under the conditions of complex road conditions, severe weather and the like are more accurate and suitable. In the technical background, the millimeter wave radar which has high resolution and can adapt to high-speed scenes is researched, and the millimeter wave radar has great significance for industrial perception and application.
Despite the above significant advantages of millimeter wave radar, problems such as high resolution performance of recognition and inability to coexist with a wide range of perceived speeds are faced due to hardware limitations. Therefore, the millimeter-wave radar with high spatial and velocity resolution used today is limited to a narrow sensing range of velocity, and the wide sensing range of velocity will bring the sacrifice of performance such as spatial and velocity resolution. The requirement for improving the sensing speed range of the high-resolution radar is provided in order to meet the requirements of high precision, high resolution and high speed application of the millimeter-wave radar for automatic driving.
At present, the millimeter wave radar still has the problem that the identified high-resolution performance and the wide sensing speed range can not coexist, and if the maximum sensing speed range can be improved from the signal processing aspect while the space and the speed resolution of the millimeter wave radar are not sacrificed, a feasible scheme can be directly provided for the application of the millimeter wave radar to enter high-speed scenes such as expressways and the like. The method has important significance for application scenes of all-weather auxiliary traffic, ramp split-combined flow early warning, driving induction in a fog area and the like of the expressway.
Disclosure of Invention
The invention provides a method for improving the maximum sensing speed of a high-resolution millimeter wave radar, which aims to solve the contradiction between the spatial resolution and the maximum measuring speed and the contradiction between the speed resolution and the maximum measuring speed.
The technical scheme adopted by the invention is that the method comprises the following steps:
And 2, the designed signal processing system processes the signals received by the millimeter wave radar frame by frame. In each frame period, respectively carrying out range FFT (distance dimension fast Fourier transform) radar ranging, dopplerFFT (Doppler dimension fast Fourier transform) radar speed measurement, CFAR (constant false alarm rate) and DOA (direction of arrival) estimation on signals of two subframes in sequence, matching an optical photo according to a timestamp, drawing a radar point cloud data graph on the optical photo, and obtaining a point cloud data set generated after DOA estimation of the two subframes.
And 3, clustering the point cloud data sets respectively according to the point cloud data sets to obtain point cloud clustering data sets of the two subframes, wherein the point cloud clustering data sets respectively comprise K0 targets and K1 targets.
And 4, matching K0 targets and K1 targets according to the point cloud clustering data set by using BEV (aerial view) information to obtain a point cloud matching data set containing successful matching and unsuccessful matching.
And 5, selecting one of two different error matrix schemes according to the point cloud matching data set and different application scenes, and calculating and obtaining a solution fuzzy strategy with the minimum error.
And 6, performing deblurring operation on the point cloud matching data set according to a deblurring strategy to obtain a deblurring radar point cloud result, and drawing the deblurring radar point cloud result on the optical photo.
Compared with the prior art, the invention has the following beneficial effects: the invention solves the problem that the maximum perception speed is lower due to the limitation of spatial resolution and speed resolution of a high-resolution millimeter wave radar for assisting intelligent driving. The method has the advantages that the space and the speed resolution of the millimeter wave radar are guaranteed, and meanwhile, the wider sensing speed detection range is realized in the signal processing level. Due to hardware limitation, on the premise that the sampling rate and the sweep frequency rising speed cannot be changed, the millimeter wave radar can measure the speed of a vehicle in a high-speed environment by improving an algorithm for optimizing signal processing while increasing the number of receiving and transmitting antennas by reducing the spatial resolution of the millimeter wave radar, and therefore the application requirement of the millimeter wave radar in a high-speed scene is met.
Drawings
Fig. 1 is a schematic diagram of a chirp configuration (1);
fig. 2 is a schematic diagram of a chirp configuration (2);
FIG. 3 is a flow chart of the velocity expansion algorithm (1);
FIG. 4 is a flow chart of the velocity expansion algorithm (2)
FIG. 5 is a schematic diagram of velocity blur;
FIG. 6 is a velocity deblurred image (1);
fig. 7 is a velocity deblurred image (2).
Detailed Description
The invention will be further elucidated and described with reference to the drawings and the detailed description. The technical features of the embodiments of the present invention can be combined correspondingly without mutual conflict.
The maximum sensing speed range is improved based on a Texas instrument TI MMWCAS-RF-EVM (AWR 2243) and MMWCAS-DSP-EVM cascade radar suite and a high-resolution mode of 12T16R (12 transmitting antennas and 16 receiving antennas).
The FMCW technique of millimeter-wave radar specifically includes: angular resolution
In the formula, delta theta represents the angular resolution, and d is between two adjacent receiving antennasThe distance is lambda is the wavelength of IF signals generated after receiving and sending signals are processed by a mixer, K is the number of receiving antennas, and theta represents the arrival angle; distance resolution
In the formula: Δ R represents the distance resolution, c represents the speed of light, and B represents the scanning bandwidth of the hardware in FMCW mode; velocity resolution
In the formula: n is the number of pulses transmitted by one transmitting antenna in a frame, T c Is the time required for all transmitting antennas to continuously transmit a round of pulse, i.e. the sweep period, so NT c Represents a frame period; maximum speed of measurement
In the design of the high-resolution millimeter wave radar, in order to improve the spatial resolution of the millimeter wave radar, an MIMO method is introduced, the number of TX transmitting antennas is increased, the number of receiving antennas N can be equivalently and multiply increased, and meanwhile, the sweep period T is c The spatial resolution (mainly delta theta) and the velocity resolution (delta V) can be reduced; but T c Will result in a maximum measurement speed v max And (4) reducing, namely reducing the maximum sensing speed of the millimeter wave radar, and reducing the sensing speed range.
When the actual speed V of a target r Greater than the maximum detection speed V of the radar max Then the velocity obtained by the dopplerFFT processing will be blurred, and the blurring velocity is defined as V a . Fuzzy velocity V of target a And the actual speed Vr satisfy the formula: v a ＝V r modV max Velocity of blur V a Is the actual speed V r For the highest detection speed V max Mod denotes the remainder, V r ＝V a +k×V max But k is an unknown coefficient. As shown in fig. 5, the millimeter wave radar is set to transmit two sub-frames in one frame for sensing the object speed,the actual speed of the vehicle is about 35km/h, but since the maximum sensing ranges of the sub-frame 1 (maximum sensing range-4.950 m/s-4.796 m/s) and the sub-frame 2 (maximum sensing range-3.322 m/s-3.218 m/s) are exceeded, a blurring phenomenon occurs in the dopplerMap II thermodynamic diagram to represent the distance-speed relationship.
A method for improving the maximum sensing speed of a high-resolution millimeter wave radar comprises the following specific steps:
As shown in fig. 1, the period of a single frame transmitted by the millimeter wave radar is 100ms, and two subframes subframe1 and subframe2 are included therein. The specific setting details are as follows:
the first 15ms sub-frame (subframe 0) is defined as Fast-Chirp sub-frame. The first 15ms subframe has N =64 loops (loop for doppler fft), and because MIMO (multiple input multiple output) technology is adopted, 12 transmitting antennas in each loop transmit chirp in turn, each chirp is configured with a leisure time idle _ time0=2.00 μ s, wherein the number of sampling points of a single chirp pulse is 256. According to the formula
The velocity resolution at this time is Δ v 0 =0.1547m/s, according to the formula
The maximum perceived velocity can be calculated as v 0max ＝4.95m/s。
The sub-frame (subframe 1) of the last 85ms is defined as a Slow-Chirp sub-frame. The sub-frame of the rear 85ms has N =64 loops, and due to the adoption of the MIMO technology, 12 transmitting antennas in each loop sequentially transmit chirp, each chirp is configured with leisure time idle _ time1=10.00 mus, and the number of sampling points of a single chirp is 256.According to the formula
The velocity resolution at this time is Δ v 1 =0.1038m/s, according to the formula
The maximum perceived velocity can be calculated as v 1max ＝3.32m/s。
For millimeter wave radar (TI MMWCAS-RF-EVM (AWR 2243) + MMWCAS-DSP-EVM), as shown in the upper left corner of fig. 2, the single pulse configuration parameters include manual configuration information such as idel _ time (idle time, which determines the sweep period, subframe1 is 2.000000us, subframe2 is 10.000000 us), ADC _ start _ time (ADC scan start time, subframe1 is 2.720000us, subframe2 is 2.720000 us), numTxAnt (number of transmit antennas, subframe1 is 12, subframe2 is 12), num _ chirp (number of pulses transmitted by one transmit antenna within one frame, subframe1 is 64, subframe2 is 64), ADC sample points (number of samples, subframe1 is 256, subframe2 is 256), subframe1 is 256, subframe2 is 256, subframe1 is 4, subframe 352 is 350, maximum distance detection parameters such as rx frame2, subframe is 310, subframe2 is 310, subframe is 310, maximum distance detection parameters are 19.3, rb _ frame2, subframe is 19, maximum distance detection parameters such as rx frame2, subframe is 19.3, subframe2, maximum distance detection parameters are 19.3, rb _ sa, rb _ frame2, subframe is 19.3/19, maximum distance detection parameters such as rx frame2, etc., maximum detection parameters are found as rb _ frame2, maximum detection parameters (rbframe), and others, etc., maximum detection parameters are found as rbframe 2, etc., maximum detection parameters (rbframe), and others, etc., maximum detection parameters such as rbframe 2, maximum detection parameters are found as rbframe 2, maximum detection parameters (rbframe 2, etc. This transmission mode is named mode3.
The calibrated, transmitted and received original signal is a four-dimensional complex signal matrix of 256 (number of sampling points in a single chirp) × 64 (number of loop antennas) × 12 (number of transmitting antennas) × 16 (number of receiving antennas) in each sub-frame period in each frame period, and the structure of the original signal at the right half of fig. 2 is shown.
And 2, the designed signal processing system processes the signals received by the millimeter wave radar frame by frame. In each frame period, respectively carrying out range FFT radar ranging, dopplerFFT radar speed measurement, CFAR constant false alarm processing and DOA estimation on signals of two subframes in sequence, matching optical photos according to time stamps, drawing a radar point cloud data graph on the optical photos, obtaining a point cloud data set generated after DOA estimation of the two subframes, and respectively naming the point cloud data set as angleEst0 and angleEst1 in the system.
As shown in the method flowcharts of fig. 3 and fig. 4, the processing is performed frame by frame, and in each frame period, for the original signals of two sub-frames: 256 x 64 x 12 x 16, in dimension 1:256 samples are subjected to a range fft and then scaled by dimension 2:64 loops (cycle) for doppler fft radar velocimetry, and then dimension 3 and dimension 4: compressing data of 12 × 16 receiving and transmitting antenna groups into 192 dimensions (MIMO technology), then performing CFAR (constant false alarm rate) on 256 × 64 × 192 three-dimensional data, detecting P targets, and outputting a detection _ results structure, wherein the structure comprises the following information: target distance, target velocity, target to 1 x 192 dimensional data containing angle information, and target SNR (signal to noise ratio). And then carrying out DOA estimation on the detection _ results structure, estimating Q0 target points, and outputting an angleEst structure, namely a point cloud data set, wherein the angleEst structure comprises information: target distance, target velocity, target azimuth, target pitch, and target SNR (signal-to-noise ratio).
The processing flow of the two subframes (subframe 0 and subframe 1) is the same.
And 3, respectively clustering the point cloud data sets (angleEst 0 and angleEst 1) generated after the DOA estimation of the two subframes to obtain point cloud clustering data sets of the two subframes and respectively containing K0 and K1 targets.
Firstly, converting a target to a rectangular coordinate system (xyz coordinate) according to a distance, an azimuth angle and a pitch angle for an angleEst structural body output by DOA estimation of two subframes, and then performing DBSCAN clustering in three dimensions of x coordinate, y coordinate and SNR (signal to noise ratio) in a BEV (beam steering angle) diagram, wherein the DBSCAN clustering process comprises two parameters: epsilon =2 (threshold of distance), minpts =5 (minimum number of samples in the field). And acquiring point cloud clustering data sets of two subframes, wherein the point cloud clustering data sets respectively comprise K0 targets and K1 targets.
And 4, matching the K0 targets and the K1 targets by utilizing BEV information according to the point cloud cluster data sets containing the K0 and the K1 targets of the two subframes to obtain a point cloud matching data set containing successful matching and unsuccessful matching.
And (3) carrying out position matching in space by utilizing point cloud cluster data sets of the two sub-frames, wherein the point cloud cluster data sets respectively comprise K0 targets and K1 targets, and utilizing the distance and the coordinates of the targets.
And 5, according to a point cloud matching data set containing successful matching and unsuccessful matching, selecting one of two different provided error matrix schemes according to different application scenes, and calculating and obtaining a solution fuzzy strategy with the minimum error.
Two different error matrix schemes, the first scheme is as follows: a 3 x 3 error matrix is adopted in a low-speed environment, a theoretical ambiguity-solving speed interval is controlled to be-9.9 m/s to +9.9 (m/s) (35.64 km/h), and the method is suitable for scene detection of vehicles and pedestrians; the second set of schemes is: in a high-speed environment, a 5-by-5 error matrix with known orientation labels (namely marks indicating that objects are close to or far away from the millimeter wave radar) is adopted, and a theoretical deblurring speed interval is controlled to be 0m/s to +16.61 (m/s) (59.80 km/h). For a roadbed millimeter wave application scene, two sets of millimeter wave radar devices can be deployed on two sides of a road for time-sharing multiplexing.
The size of the error matrix is set, the theoretical maximum ambiguity resolution speed range is calculated according to the Chinese remainder theorem, the configuration of the error matrix is sequentially assisted, the Chinese remainder theorem adopts information received by twice sampling, whether the vehicle is the same is judged according to each subframe signal and clustering according to position information, and the speed of the vehicle is further recovered.
According to the Chinese remainder theorem, i.e. for equation (S)
Wherein m is 1 ，m 2 ，...，m n Two by two are relatively prime, then can pass through 1 ，a 2 ，...，a n The value of x is obtained.
According to FIGS. 1 and 2, in the present invention, two different T's are defined c Two chirp of (a), V corresponding to the two chirp max V of different, one and the same target r V detection under these two chirp conditions a And also different. According to the Chinese surplusTheorem (if a natural number respectively takes the remainder for a group of co-prime factors, the original natural number can be calculated under the condition that the remainder and the co-prime factors are known), and V can be calculated under the two chirp numbers a Restoring the actual velocity V of the target r 。
According to the algorithm flows of fig. 3 and 4, the same vehicle must accumulate at least two samples (corresponding to fast-chirp and slow-chirp configured in step 1) in one frame period, recover the real speed within the allowable error range after target matching, and assume that the fuzzy speed array sensed by two subframes is V when the real speed of the target vehicle is recovered 1 ，V 2 。
The velocities perceived by different parts of the same object can differ slightly, so the blur velocity exists in an array. The maximum perceptual speed range spans corresponding to fast-chirp and slow-chirp are known as: v scale1 ＝2×v 1max ＝6.64m/s，V scale2 ＝2×v 0max ＝9.90m/ s In the formula v 1max And v 0max Is the maximum perceptual speed for fast-chirp and slow-chirp.
According to two different error matrix schemes proposed at the beginning of step 5, tables 1 and 2 show a certain frame 3 x 3 error matrix for processing 20km/h single target identification data, and a certain frame 5 x 5 error matrix for processing a known orientation label of 50km/h single target identification data:
table 1:3 x 3 error matrix (target 0.6522)
| dis (error) | V 1 -1×V scale1 | V 1 | V 1 +1×V scale1 |
| V 2 -1×V scale2 | 6.1206 | 15.705 | 108.1583 |
| V 2 | 145.5953 | 31.689 | 0.6522 |
| V 2 +1×V scale2 | 469.0949 | 231.69 | 77.1711 |
Table 2: the 5 x 5 error matrix towards the tag is known (the vehicle is known to be approaching the centre of the radar, i.e. the radial velocity is negative, so in form it is also a 3 x 3 matrix, data 1.0791 is the target)
| dis (error) | V 1 -2×V scale1 | V 1 -1×V scale1 | V 1 |
| V 2 -2×V scale2 | 73.1625 | 224.7138 | 459.1336 |
| V 2 -1×V scale2 | 1.0791 | 29.1399 | 140.0694 |
| V 2 | 113.0205 | 17.5910 | 5.0300 |
The design of the error matrix refers to a theoretical maximum deblurrable speed range, and the theoretical maximum deblurrable speed range is determined by the Chinese remainder theorem.
Dis (error) in the above table is described as: fast-chirp and slow-chirp two-subframe perceived fuzzy velocity array V 1 ，V 2 After transformation according to the formula (strategy) described in the first and second rows of the matrix, the difference between the mean values of the two arrays is obtained. The smaller dis (error), the more accurate the deblurring formula (strategy) is represented, and the better the effectiveness. And selecting a group of solution fuzzy formulas (strategies) with the minimum error, acting the solution fuzzy formulas (strategies) on the fuzzy speed arrays of the two groups of point clouds of fast-chirp and slow-chirp, and taking the mean value to obtain the real speed.
And 6, performing deblurring operation on the point cloud matching data set according to a deblurring strategy with the minimum error to obtain a deblurring radar point cloud result, and drawing the deblurring radar point cloud result on the optical photo.
Reference may be made to fig. 5, 6 and 7, where fig. 5 illustrates a speed blurring phenomenon occurring when the maximum perceived speed increasing method of the present invention is not used, and a point cloud speed in the diagram is a speed after blurring. The two left graphs (Doppler map I) are the relationship between the Receive Power and Range, and are used for reflecting the distance between the radar and the sensing target; the middle two graphs (DopplerMap II) are thermodynamic diagrams between Range and Velocity, the other parameter of the thermodynamic diagram, color, is Receive Power, and white areas in the graphs indicate that detection targets appear in the coordinate areas of distance and Velocity; the four images on the right represent a point cloud-photo projection image of subframe, a spatial point cloud distribution image of subframe, a point cloud-photo projection image of subframe2 and a spatial point cloud distribution image of subframe2 from top to bottom, respectively (the upper part of each image records a time stamp of a picture or radar). From fig. 5, it can be seen that when the experimental perception target is a car running at 35km/h towards the radar, the millimeter wave radar has a distinct speed ambiguity phenomenon in DopplerMap. After the method for improving the maximum sensing speed of the invention is used, the result is as follows: FIG. 6 is a projection diagram of the deblurred point cloud-photo, the point cloud is projected to an optical image, FIG. 7 is a distribution diagram of the spatial point cloud after deblurring, the deblurring speed of the actual data is-10.23 m/s, and the actual data is close to the true value.
In order to further prove the effect of the invention, a GNSS combined inertial navigation system is installed on a single-target car, original data obtained by GNSS is converted, longitude and latitude, north-south speed and east-west speed are extracted, and the original data are projected to a connection line of a car and a millimeter wave radar to be used as a true value (Ground truth); matching is performed according to the timestamp of the internal frame of the device, and the true value (Ground channel) is compared with the deblurring speed (deblurr) of the single-target car processed by the method for improving the maximum sensing speed of the high-resolution millimeter wave radar of the invention to obtain an Error (Error), as shown in table 3:
TABLE 3
| True value km/h) | 4.61 | 13.99 | 17.83 | 25.70 | 35.47 | 45.16 |
| Method (km/h) | 4.64 | 13.92 | 17.77 | 25.95 | 35.96 | 44.78 |
| Error (%) | 0.66 | 0.47 | 0.32 | 1.00 | 1.41 | 0.84 |
The above-described embodiments are merely preferred embodiments of the present invention, which should not be construed as limiting the invention. Various changes and modifications may be made by one of ordinary skill in the pertinent art without departing from the spirit and scope of the present invention. Therefore, the technical scheme obtained by adopting the mode of equivalent replacement or equivalent transformation is within the protection scope of the invention.
Claims (5)
1. A method for improving the maximum sensing speed of a high-resolution millimeter wave radar is characterized by comprising the following steps:
step 1, configuring parameters on a millimeter wave radar evaluation board based on a frequency modulation continuous wave FMCW technology, configuring a transmission mode as a 12T16R mode, and particularly configuring a transmission mode of transmitting two subframes with different idle _ time in a single frame period in a single frame pulse chirp time;
step 2, the designed signal processing system processes the signals received by the millimeter wave radar frame by frame to obtain a point cloud data set generated after DOA estimation of two subframes;
step 3, clustering the point cloud data sets respectively according to the point cloud data sets to obtain point cloud clustering data sets of two subframes respectively containing K0 and K1 targets;
step 4, matching K0 targets and K1 targets according to the point cloud clustering data set by using the aerial view BEV information to obtain a point cloud matching data set containing successful matching and unsuccessful matching;
step 5, selecting an error matrix scheme according to the point cloud matching data set and different application scenes, and calculating and obtaining a fuzzy solving strategy with the minimum error;
and 6, performing deblurring operation on the point cloud matching data set according to a deblurring strategy to obtain a deblurring radar point cloud result, and drawing the deblurring radar point cloud result on the optical photo.
2. The method for improving the maximum perception speed of the high-resolution millimeter-wave radar according to claim 1, wherein the method comprises the following steps: the frame-by-frame processing in step 2 specifically includes: in each frame period, respectively carrying out distance dimensional fast Fourier transform range FFT radar ranging, doppler dimensional fast Fourier transform doppler FFT radar speed measurement, constant false alarm rate detection CFAR and DOA estimation on the direction of arrival of signals of two subframes in sequence, matching an optical photo according to a timestamp, and drawing a radar point cloud data graph on the optical photo.
3. The method for improving the maximum perception speed of the high-resolution millimeter-wave radar according to claim 1, wherein the method comprises the following steps: step 2, information contained in the point cloud data set: target distance, target speed, target point azimuth, target point pitch angle and target signal-to-noise ratio SNR.
4. The method for improving the maximum sensing speed of the high-resolution millimeter wave radar according to claim 3, wherein the method comprises the following steps: step 3, clustering treatment, which comprises the following specific processes:
and converting the target to a rectangular coordinate system according to the distance, the azimuth angle of the target point and the pitch angle of the target point, and then performing DBSCAN clustering in three dimensions of x coordinate, y coordinate and signal-to-noise ratio (SNR) in the aerial view BEV.
5. The method for improving the maximum perception speed of the high-resolution millimeter-wave radar according to claim 1, wherein the method comprises the following steps: step 5 there are two sets of error matrix schemes, which are:
controlling a theoretical ambiguity resolution speed interval to be-9.9 m/s to +9.9 m/s by adopting a 3-by-3 error matrix in a low-speed environment;
and in a high-speed environment, a 5-by-5 error matrix with known orientation to the label is adopted to control the theoretical deblurring speed interval to be 0m/s to +16.61 m/s.
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| CN116449329A (en) * | 2023-04-27 | 2023-07-18 | 深圳承泰科技有限公司 | Method, system, equipment and storage medium for disambiguating speed of millimeter wave radar |
| CN117079245A (en) * | 2023-07-05 | 2023-11-17 | 浙江工业大学 | Traffic road target identification method based on wireless signals |
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2022
- 2022-09-06 CN CN202211083253.0A patent/CN115436930A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116449329A (en) * | 2023-04-27 | 2023-07-18 | 深圳承泰科技有限公司 | Method, system, equipment and storage medium for disambiguating speed of millimeter wave radar |
| CN117079245A (en) * | 2023-07-05 | 2023-11-17 | 浙江工业大学 | Traffic road target identification method based on wireless signals |
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