Speed ambiguity resolution method suitable for 4D millimeter wave radar imaging

Abstract

The invention discloses a speed ambiguity resolution method suitable for 4D millimeter wave radar imaging. The method comprises the steps of 1, acquiring radar original target sampling data, and performing two-dimensional fast Fourier transform processing to obtain a target distance-Doppler matrix; carrying out constant false alarm rate detection on the target distance-Doppler matrix; 2, searching an optimal representative point as a speed representative point of the target; 3, adopting a hypothetical phase compensation speed ambiguity resolution algorithm for the speed representative point of the target to obtain a speed and a phase compensation value corresponding to the representative point; step 4, compensating all target points with phase compensation values calculated according to the representative points, and performing DOA estimation to obtain azimuth angle values; 5, coordinate transformation is carried out according to the obtained target point speed and azimuth angle value, the real position of the target under the three-dimensional coordinate system is obtained, and the representative point speed represents the real speed of the target. According to the invention, the detection capability of the MIMO radar imaging system on a high-speed target can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar imaging, and in particular relates to a velocity deambiguation method suitable for 4D millimeter wave radar imaging. Background Art

[0002] In radar systems, Doppler shift is a means of measuring the speed of a target relative to the radar. However, when the Doppler shift generated by the speed of a target exceeds the maximum frequency that the radar system can process, velocity ambiguity occurs. Although MIMO radar can provide richer target information, such as additional information such as target height and profile, the unambiguous detection speed range of the radar system is significantly reduced due to the working principle of MIMO radar. Incorrect target speed estimation will affect the accuracy of target angle estimation, so resolving velocity ambiguity is critical for MIMO imaging radar.

[0003] Methods based on pulse repetition frequency (PRF) variation are usually common solutions to velocity ambiguity problems, including the Chinese Remainder Theorem (CRT) method, table lookup method, and one-dimensional set algorithm.

[0004] The Chinese Remainder Theorem (CRT) is an effective method to eliminate phase uncertainty in Doppler shift detection. It obtains the deambiguation result by establishing a set of congruence equations between the measured value and the target speed. However, in the classic CRT, the greatest common divisor (GDC) of any pair of PRFs is equal to 1. Although this method of resolving Doppler ambiguity is quite accurate and fast, its accuracy will drop rapidly when the remainder has some deviations due to noise. The classic table lookup method quickly matches the actually observed Doppler shift by pre-calculating and storing the velocity-Doppler shift relationship under different conditions to find the true speed of the target. Although this method is simple and easy to implement in engineering, the table storage requires a lot of storage space. In addition, in the lookup table, the degrees are divided by distance units, and a lot of time is wasted on unnecessary numerical calculations. It is difficult to meet the requirements of real-time processing and difficult to adapt to complex scenarios. The computational complexity of the one-dimensional set algorithm will increase exponentially with the increase in the number of targets within the radar detection range, making it difficult to meet the requirements of radar real-time detection.

[0005] The hypothetical phase compensation (HPC) velocity deambiguation algorithm can estimate the true velocity of the target under large velocity ambiguity periods and perform velocity interpretation and deambiguation processing within a single frame. It is suitable for MIMO radar systems, but for imaging, this method can decompose a single target into multiple target points due to its high spatial resolution, and this high resolution leads to an increase in the number of target points. In the process of processing each target point to eliminate velocity ambiguity, if the HPC velocity deambiguation algorithm is applied to each target point, the algorithm time complexity will be significantly increased, thereby reducing the system processing speed. In addition, the higher energy of the sidelobe points may cause velocity expansion, resulting in noise in the point cloud data.

[0006] The patent document "A method, device and millimeter wave radar for speed deambiguation of radar" (application date: August 15, 2022, announcement number: CN115421134B) discloses a method, device and millimeter wave radar for speed deambiguation of radar. The method receives the corresponding echo signals through at least four antennas, and calculates the estimated speed of the target in combination with the phase relationship of each antenna. According to the estimated speed of the target, combined with the known maximum fuzzy speed, the ambiguity is obtained; based on the ambiguity, the reference speed of the target is calculated as the true speed after Doppler shift. However, this method is not suitable for imaging radar, and multiple frames of data are required for deambiguation.

[0007] The patent document "A method for solving speed ambiguity by using entropy in millimeter-wave MIMO traffic radar" (application date: February 27, 2020, announcement number: CN111308437B) discloses a method for solving speed ambiguity by using entropy in millimeter-wave MIMO traffic radar. This method obtains the preliminary distance, speed, and angle information of each target, calculates the unambiguous speed of the target, establishes an optimization model, and obtains 2L groups of different compensation phases; obtains the minimum entropy value e, and calculates the corresponding unambiguous speed at this time according to its position. This method requires processing of each target point, but for 4D millimeter-wave radar, because of its high spatial resolution, one target is usually identified as multiple target points. If the above processing is performed on each target point, the real-time performance of the algorithm will be greatly reduced. Summary of the invention

[0008] In order to overcome the shortcomings of the above-mentioned prior art, the purpose of the present invention is to provide a velocity deambiguation method suitable for 4D millimeter-wave radar imaging, which adopts a hypothetical phase compensation velocity deambiguation algorithm and uses single-frame data to solve the velocity deambiguation problem of moving targets with a large velocity ambiguity period, so as to enhance the detection capability of the MIMO radar imaging system for high-speed targets.

[0009] In order to achieve the above object, the technical solution adopted by the present invention is:

[0010] A velocity deambiguation method suitable for 4D millimeter wave radar imaging, comprising the following steps;

[0011] Step 1: The 4D millimeter wave radar generates a difference frequency signal after mixing the received and sent Chirp signals. The difference frequency signal is processed by two-dimensional fast Fourier transform (2D-FFT) to obtain the target range-Doppler (RDM) matrix.

[0012] And through constant false alarm detection, the target point distance, target point speed and phase information are obtained;

[0013] Step 2: Find the best representative point as the velocity representative point of the target, which is located near the midpoint of the line connecting the peak point of the target point cloud image and the geometric center point of the target RDM;

[0014] Step 3: Use the assumed phase compensation speed defuzzification algorithm for the speed representative point of the target to obtain the speed and phase compensation value corresponding to the speed representative point;

[0015] Step 4: Select the velocity ambiguity period corresponding to the peak point of the angular power spectrum of the target point under the assumption in step 3, compensate all target points with the phase compensation value calculated according to the velocity representative point, and perform DOA estimation to obtain the azimuth angle value;

[0016] Step 5: According to the obtained target point speed and azimuth angle values, coordinate transformation is performed to obtain the true position of the target in the three-dimensional coordinate system. The speed representative point speed represents the true speed of the target.

[0017] The step 1 is specifically as follows:

[0018] Step (1): Sample the difference frequency signal generated by mixing the received and transmitted Chirp signals of the 4D millimeter wave radar. The sampling results are stored in the form of a matrix. The ADC module implemented based on FPGA performs sampling. The specific sampling parameter calculation is shown below.

[0019] The number of radar range FFT points is determined by the maximum detection range and range resolution of the radar. The formula can be expressed as:

[0020]

[0021] Where N FFTR is the number of FFT points, R max Represents the maximum detection distance of the radar, R res is the distance resolution.

[0022] ADC sampling rate F s According to the Nyquist sampling theorem, the effective sampling frequency f of the radar is IFmax The maximum detection range of the radar is as follows:

[0023]

[0024] Where f IFmax represents the effective sampling frequency, c = 3 × 10 8 m / s represents the speed of light, and S is the frequency modulation slope of the Chirp signal. Under the ADC complex sampling condition, the ADC sampling frequency F S Just satisfy F S ≥f IFmax , the intermediate frequency signal can be restored, so the sampling frequency needs to meet the following requirements:

[0025]

[0026] Step (2): performing FFT transformation on each row and each column of the matrix to obtain a range-Doppler spectrum, and estimating the range and speed of the target by analyzing the target range-Doppler spectrum (RDM);

[0027] Step (3): Use the ordered statistics (OS-CFAR) detection algorithm to perform constant false alarm detection on the target RDM; obtain the target point distance, target point speed and phase information.

[0028] The step (2) is specifically:

[0029] The time delay of the echo is calculated through the spectrum of the difference frequency signal to determine the target distance. The transmitted Chirp signal is expressed by the following formula:

[0030] S TX =Aexp(j×(2πf c t+πkt 2 )), t∈(0,T c )

[0031] Among them, f c is the carrier frequency, T c is the period, A is the amplitude, is the slope of frequency versus time, B is the signal bandwidth, and t is the time coordinate of the signal frequency changing with time within one cycle;

[0032] The difference frequency signal is generated by mixing the target reflected signal with the reference signal, and is expressed as follows:

[0033] S IF =Aexp(j×(2π·kτ·t+2π·f c τ-πkt 2 ))

[0034] in is the round-trip delay of the electromagnetic wave, c is the speed of light, R represents the distance between the target and the radar, the frequency of the difference frequency signal is linearly related to the delay τ, and the difference frequency signal is subjected to an FFT to obtain the signal frequency of the difference frequency signal, and the distance R of the target is calculated. The formula is:

[0035]

[0036] In digital domain processing, the number of sampling points is N r , the sampling rate is F s Under the sampling condition of , the distance resolution R of the distance information obtained after digital signal processing res , expressed as:

[0037]

[0038] For the same moving target, perform FFT on two adjacent difference frequency signals separately to obtain different phase information. The one-dimensional FFT peaks of the two difference frequency signals are the same, but the phases are different. The speed information of the target can be obtained based on this phase difference.

[0039] The process of using the two-dimensional FFT algorithm to process a frame of difference frequency signal of the target is expressed as:

[0040] First, the echo signals of multiple chirps in each frame are sampled and the sampling results are stored in the form of a matrix. The sampling results of each chirp signal are stored in the matrix row by row. Then, each row and column of the matrix is ​​transformed by FFT to obtain the range-Doppler spectrum. By analyzing the range-Doppler spectrum (RDM), the distance and speed of the target are estimated.

[0041] The step (3) is specifically:

[0042] 1) First, the threshold of the measured unit in the distance dimension of the RDM matrix is ​​solved. According to the principle of CACFAR algorithm, the detection threshold of the measured unit in the distance dimension is: Among them, M is the number of training units, rx i , r y i, respectively, are the training units on the left and right of the unit under test, and K is the threshold factor;

[0043] 2) Then, the threshold of the unit under test in the velocity dimension of the RDM matrix is ​​solved in the same way as the distance dimension. The threshold of the unit under test in the velocity dimension is dx i , dy are the training units on the upper and lower sides of the tested unit respectively;

[0044] 3) After calculating the distance dimension and speed dimension thresholds, the unit under test is tested for crossing the threshold. The crossing threshold test can be expressed as (A>T r )&(A>Td ), A is the power of the unit under test in RDM. Only when it exceeds the distance dimension and speed dimension threshold at the same time, it is identified as a target;

[0045] After CFAR, the distance, velocity and phase information of the target point are obtained;

[0046] The speed and phase information of the target point are used as the input for subsequent speed deambiguation. The distance of the target point corresponds to the distance index, and the speed of the target point corresponds to the Doppler index. The speed of the target is calculated by the Doppler frequency shift of the target. The phase information and the echo intensity of the target signal are the power amplitude of the target point.

[0047] The step 2 is specifically as follows:

[0048] First, find the point with the strongest energy in the point cloud image of the target, that is, the peak point, and connect it with the target RDM geometric center point to get the midpoint. The target RDM geometric center point is obtained by obtaining the RDM after 2D FFT, then extracting the target area after target detection, and calculating the geometric center point. Then traverse all target points and select the point in RDM closest to this midpoint as the target's velocity representative point;

[0049] The geometric center point of the RDM is obtained by RDM calculation, and then the optimal point is found near the midpoint between the geometric center point of the RDM and the peak point.

[0050] In step 3, a velocity defuzzification algorithm using a hypothetical phase compensation velocity is used for the velocity representative point of the target. The method is as follows:

[0051] First, the detection phase shift of the CFAR detection output of the target point Enumerate all possible phase values ​​that may need to be compensated:

[0052]

[0053] Secondly, according to formula (1), the radar data is compensated with each assumed phase compensation value in turn, and the compensated data is expressed as

[0054]

[0055] Among them, S i Represents the signal data in the i-th channel, i starts from 2, Indicates the phase value that needs to be compensated. At the same time, data is extracted from the same index position in each channel RDM to form a channel data set as follows:

[0056]

[0057] The value of N depends on the number of transmitting antennas M TX To calculate, N satisfies:

[0058] |N|<=(M ТХ -1) / 2 (4)

[0059] When the number of transmitting antennas is odd;

[0060] |N|<=(M TX / 2)-1 (5)

[0061] When the number of transmitting antennas is even;

[0062] Use formulas (4) and (5) to get the velocity blur period N, where M TX is the number of transmitting antennas;

[0063] Then, according to the N value, all HPC situations are listed, and the radar data is compensated in turn with each assumed phase compensation value, H satisfies formula (2), and then FFT operation is performed on each compensated data (2N+1 in total) to obtain the angular power spectrum under each assumed condition;

[0064] H=H -N ,...,H k ,...,H N (6)

[0065] By selecting the peak point in the angular power spectrum, the true speed of each speed representative point is determined, and the speed corresponding to the speed representative point is the true speed of the entire target.

[0066] The step 4 is specifically as follows:

[0067] When the RDM containing the target speed and distance information is processed by CFAR detection, the estimated value of the target speed and the Doppler phase difference are obtained;

[0068] Set the result sequence passing through the CFAR target point to X (m , n) (0 <m<N TX ,0 <n<N RX ), then the phase compensation satisfies the formula:

[0069] Where X(m,n) is the original signal matrix, is the phase factor, m is the transmit antenna index, n is the receive antenna index, and N TX is the number of transmit antennas, N RX is the number of receiving antennas;

[0070] Use angle-dimensional FFT for DOA estimation. When using FFT to process signals, the direction information of the target signal is "mapped" to the frequency domain, and the direction of arrival of the signal is determined by calculating the energy spectrum at different angles.

[0071] The formula is:

[0072]

[0073] Where M represents the number of receiving antennas;

[0074] The result of FFT processing is an angle spectrum P(θ). The arrival angle of the target is estimated by finding the peak position of the power spectrum. In the calculated angle spectrum P(θ), the angle corresponding to the peak is the arrival angle of the target.

[0075] The step 5 is specifically as follows:

[0076] The target point distance, speed and phase information are used as the input of the improved hypothetical phase compensation speed ambiguity resolution algorithm model. Based on the representative point selection method of the target RDM geometric center point and peak point, the representative points of the three targets are obtained, which are respectively recorded as: representative point 1, representative point 2 and representative point 3. For each representative point, RDM provides its speed information V cfar1 , V cfar2 , V cfar3 , and phase information

[0077] The HPC velocity ambiguity resolution algorithm first obtains the assumed velocity ambiguity period N according to the number of radar transmitting antennas, and then enumerates all possible assumed phase compensation (HPC) situations for each velocity representative point based on this period and known velocity information;

[0078] Next, each assumed phase compensation value is applied to the target point information to calculate the angular power spectrum under various assumed conditions. By selecting the assumption of the peak point in the angular power spectrum, the true speed of each speed representative point is determined. The speed corresponding to the speed representative point is the true speed of the entire target.

[0079] Based on the velocity ambiguity periods corresponding to the peak points, which are N1, N2 and N3, the phase compensation value of each target is calculated, and phase compensation is performed on all target points according to their respective deambiguation periods. Then, DOA estimation (such as angle-dimensional FFT and DBF) is performed to accurately obtain the true azimuth angle value of each target.

[0080] Finally, the real distance of the target is calculated according to the relationship between the target point distance index and the radar distance resolution, and the coordinate transformation is performed to form a point cloud image with target distance and angle information.

[0081] Phase information Perform azimuth-elevation FFT to obtain the target's AEM matrix;

[0082] Afterwards, the AEM is subjected to a constant false alarm detection method based on the normalized zero-point angle power spectrum to obtain the target azimuth and elevation angle information;

[0083] Finally, the target’s distance, azimuth, and pitch angle information are transformed into coordinates to generate a three-dimensional point cloud image of the target space. In the target point cloud image, target points with different speeds will be represented by different colors, that is, the four-dimensional information (distance, altitude, azimuth, and speed) of the target is displayed in the same image.

[0084] The method is applied to intelligent transportation systems and 4D millimeter wave radar imaging systems.

[0085] Beneficial effects of the present invention:

[0086] First, the representative point selection method proposed in the present invention improves the processing efficiency of the speed defuzzification algorithm by selecting representative points in the RDM target point cloud cluster to implement speed fuzzy periodic solution instead of global point enumeration processing.

[0087] Second, the velocity defuzzification method proposed in the present invention ensures that points belonging to the same target point cloud image have the same velocity, thereby improving the stability of the velocity defuzzification method.

[0088] Third, the velocity defuzzification method proposed in the present invention can not only output the accurate velocity value of the target, but also output the accurate phase compensation value, ensuring that the position of the target point cloud image is consistent with the real position of the target, thereby improving the quality of the point cloud image. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 It is a schematic diagram of the difference frequency signal of the present invention.

[0090] Figure 2 It is a system block diagram of the present invention.

[0091] Figure 3 It is a schematic diagram of representative point selection of the present invention.

[0092] Figure 4 The figure is a schematic diagram of the implementation process of the present invention.

[0093] Figure 5 This is a test scene graph for the present invention.

[0094] Figure 6 This is a test effect diagram of the present invention; Figure 6 (a) is a schematic diagram without velocity defuzzification; Figure 6 (b) is a schematic diagram of the original HPC velocity deblurred spatial stereo point cloud image; Figure 6(c) is the top view of the original HPC velocity deblurred spatial stereo point cloud image; Figure 6 (d) is a schematic diagram of the improved HPC speed deblurred spatial stereo point cloud image; Figure 6 (e) A top view of the spatial stereo point cloud image with improved HPC speed deblurring.

[0095] Figure 7 This is a comparison chart of the time complexity of the methods.

[0096] Figure 8 Schematic diagram of two-dimensional FFT algorithm.

[0097] Fig. 9 This is a schematic diagram of constant false alarm detection. DETAILED DESCRIPTION

[0098] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0099] like Figure 1 , Figure 2 As shown, a velocity deambiguation method suitable for 4D millimeter-wave radar imaging;

[0100] (1) The range-Doppler (RDM) matrix detection part first performs a two-dimensional fast Fourier transform (2D-FFT) on the radar raw sampling data to obtain the target RDM matrix.

[0101] Next, the ordered statistics (OS-CFAR) detection algorithm is used to perform constant false alarm detection on the target RDM to obtain the target point distance, speed and phase information.

[0102] According to the proposed representative point selection method based on the combination of the target RDM geometric center point and the peak point, the representative points of the three targets are obtained, which are respectively recorded as: representative point 1, representative point 2 and representative point 3. RDM provides its speed information V cfar1 , V cfar2 , V cfar3 , and phase information The velocity and phase information of the target point are used as the input for subsequent velocity defuzzification;

[0103] (2) The representative point selection part based on the combination of the target RDM geometric center point and the peak point first finds the midpoint of the line connecting the target point cloud image peak point and the target RDM geometric center point, then traverses all target points and selects the point in the RDM closest to the midpoint as the representative point of the target;

[0104] The advantage of selecting representative points based on the geometric center point of the target in the radar range-Doppler map (RDM) is that it can provide a point that can better represent the overall position of the target. When the radar position is close to both sides of the road to observe vehicle targets, the target points obtained may present an "L"-shaped arrangement pattern. In this layout, the white dot represents the geometric center point obtained from the target RDM analysis. The method of selecting the peak point as the representative point can reflect the highest energy point of the target, but may not reflect the exact position of the target. Therefore, the present application proposes a method of selecting representative points based on the geometric center point and peak point of the target RDM to obtain representative points for each of the three targets.

[0105] (3) The assumed phase compensation velocity deambiguation part of the representative point,

[0106] |N|=(M TX / 2)-1 (1)

[0107] The velocity blur period N is obtained using formula (1), where M TX is the number of transmitting antennas, using a 12-transmit 16-receive antenna array, the velocity ambiguity period is 5. Then, according to the N value, all assumed phase compensation conditions are listed, and each assumed phase compensation value is used to compensate the radar data in turn. Then, FFT operation is performed on each compensated data to obtain the angular power spectrum under each assumed condition;

[0108] The assumption that the peak point in the angular power spectrum is selected, and the corresponding speed and the angle obtained are the true values ​​of the representative point;

[0109] (4) The phase compensation part of the representative point and the subordinate point compensates all the target points with the same phase compensation value as the representative point to obtain the true azimuth angle value of the target point;

[0110] (5) The coordinate conversion part performs coordinate conversion based on the obtained target point speed and azimuth angle value to obtain the real position of the target in the three-dimensional coordinate system. The representative point speed represents the real speed of the target, thereby realizing three-dimensional imaging of the target.

[0111] Figure 3 This is a schematic diagram of representative point selection of the present invention. Selecting the peak point as the representative point can reflect the highest energy point of the target, but may not reflect the exact position of the target. Although selecting the target RDM geometric center point is closer to the true geometric center point of the target, this point may also introduce errors because it is located in the radar sidelobe area.

[0112] Therefore, if Figure 3As shown in the figure, the red dot indicates the actual geometric center point of the target. Since the geometric center points of different moving targets are different, for example, a car has a geometric center point located inside the car, which cannot be directly detected by the radar. Therefore, the strategy adopted is to find an optimal representative point, which is located near the midpoint of the line connecting the target peak point and the target RDM geometric center point, and use this point as the representative point. This method can solve the shortcomings of selecting a single representative point. Although the peak point as the highest energy point of the target does indicate the strongest reflection position of the target, this position is not always consistent with the geometric center of the target. The geometric center point is not always located in the area with the highest target energy, because MIMO radar has the characteristics of high range resolution and velocity resolution, which will cause the target energy to diffuse, resulting in the point being likely to be located in the radar sidelobe area. If such a geometric center point is selected as the representative point, it may introduce errors in the velocity ambiguity resolution process, affecting the accuracy of phase compensation, and thus affecting the imaging results. Therefore, the strategy adopted in this paper is to find an optimal representative point, which is located near the midpoint of the line connecting the target peak point and the target RDM geometric center point, and use this point as the representative point.

[0113] The implementation process of the function of the present invention is as follows Figure 4 The specific steps are as follows:

[0114] Step 1: Perform constant false alarm detection on the target range-Doppler (RDM) matrix to obtain the range index, Doppler index and power amplitude of the target point;

[0115] Step 2: Find the best representative point as the velocity representative point of the target, which is located near the midpoint of the line connecting the peak point of the target point cloud image and the geometric center point of the target RDM;

[0116] Step 3: Use the assumed phase compensation velocity defuzzification algorithm for the representative point of the target to obtain the velocity and phase compensation values ​​corresponding to the representative point;

[0117] Step 4: The speed of all target points is the speed of the representative point, and all target points are compensated with the phase compensation value calculated based on the representative point;

[0118] Step 5: According to the obtained target point speed and azimuth angle values, coordinate transformation is performed to obtain the real position of the target in the three-dimensional coordinate system. The representative point speed represents the real speed of the target.

[0119] The step 1 is specifically as follows:

[0120] Firstly, the radar raw sampling data is processed by two-dimensional fast Fourier transform (2D-FFT) to obtain the target RDM matrix;

[0121] The two-dimensional FFT algorithm calculates the delay of the echo through the spectrum of the difference frequency signal, thereby determining the target distance. The transmitted Chirp signal is expressed by the formula;

[0122] S TX =Aexp(j×(2πf c t+πkt 2 )), t∈(0,T c )

[0123] Where B is the signal bandwidth, f c is the carrier frequency, T c is the period, A is the amplitude, is the slope of frequency versus time;

[0124] The difference frequency signal generated by mixing the target reflected signal with the reference signal is expressed by the formula:

[0125] S IF =Aexp(j×(2π·kτ·t+2π×f c τ-πkt 2 ))

[0126] in is the round-trip delay of the electromagnetic wave, c is the speed of light, and R represents the distance between the target and the radar. It can be seen that the frequency of the difference frequency signal is linearly related to the time delay τ. Therefore, an FFT is performed on the difference frequency signal to obtain its signal frequency and calculate the distance R of the target. The formula is:

[0127]

[0128] Since the intermediate frequency signal needs to be sampled in the actual processing, it is processed in the digital domain and the number of sampling points is N. r , the sampling rate is F s Under the sampling condition of , the distance resolution R of the distance information obtained after digital signal processing res , expressed as:

[0129]

[0130] The radar estimates the target speed by calculating the Doppler frequency shift between the difference frequency signals of at least two echoes. For the same moving target, due to the modulation period T c The difference frequency is very small, so the change in the target distance detected by the two adjacent difference frequency signals can be ignored. By performing FFT on the two adjacent difference frequency signals separately, different phase information can be obtained. The one-dimensional FFT peaks of the two difference frequency signals are the same, but the phases are different. The speed information of the target can be obtained based on this phase difference.

[0131] According to the above principle, the process of using the two-dimensional FFT algorithm to process a frame of the target difference frequency signal is expressed as follows: Figure 8 As shown;

[0132] First, the echo signals of multiple chirps in each frame are sampled, and the sampling results are stored in the form of a matrix. Specifically, the sampling result of each chirp signal is stored in the matrix row by row;

[0133] Then, each row and column of the matrix is ​​transformed by FFT to obtain the range-Doppler spectrum. By analyzing the range-Doppler spectrum (RDM), the range and speed of the target are estimated.

[0134] Next, the ordered statistics (OS-CFAR) detection algorithm is used to perform constant false alarm detection on the target RDM;

[0135] like Fig. 9 As shown, the first step of the algorithm is to detect the signal located at the unit under test. In order to prevent the energy of the target signal from leaking into the reference unit and affecting the accuracy of the detection result, it is necessary to set several adjacent units as protection units, located in front and behind the target unit. Then select the front and back n training units on both sides of the protection unit as the input of the CFAR algorithm. According to the calculation method of the threshold value designed by the CFAR processor, the design of the CFAR processor can be classified into different categories. Commonly used methods for calculating the threshold value include mean calculation, size sorting, etc., to obtain the distance, speed and phase information of the target point;

[0136] The speed and phase information of the target point are used as the input for subsequent speed deambiguation, where the range index corresponds to the distance information of the target, and the Doppler index corresponds to the speed information of the target. The speed of the target can be calculated by the Doppler frequency shift of the target. The power amplitude of the target point represents the echo intensity of the target signal.

[0137] The step 2 is specifically as follows:

[0138] Selecting the peak point as the representative point can reflect the target's highest energy point, but may not reflect the target's accurate position. Selecting the target RDM geometric center point is closer to the target's true geometric center point, but this point may also introduce errors because it is located in the radar sidelobe area.

[0139] Through the representative point selection strategy, the advantages of these two methods are combined;

[0140] First, find the point with the strongest energy in the target point cloud image, that is, the peak point, and the midpoint obtained by connecting the target RDM geometric center point. The target RDM geometric center point is obtained by obtaining the RDM after 2D FFT, extracting the target area after target detection, and calculating the geometric center point. Then traverse all target points and select the point in RDM closest to this midpoint as the representative point of the target.

[0141] The geometric center point of the RDM is obtained through RDM calculation, and then the best point is found near the midpoint between the geometric center point of the RDM and the peak point (this best point belongs to the RDM and can be considered as one of the points of the RDM).

[0142] The step 3 is specifically as follows:

[0143] The velocity representative point of the target is subjected to the velocity defuzzification algorithm using the assumed phase compensation method as follows:

[0144] First, the detection phase shift of the CFAR detection output of the target point Enumerate all possible phase values ​​that may need to be compensated:

[0145]

[0146] Secondly, according to formula (1), the radar data is compensated with each assumed phase compensation value in turn, and the compensated data is expressed as

[0147]

[0148] Among them, S i represents the signal data in the i-th channel, i starts from 2, because only non-initial codes require Doppler phase compensation, Indicates the phase value that needs to be compensated. At the same time, extract data from the same index position in each channel RDM to form a channel data set as follows:

[0149]

[0150] The value of N depends on the number of transmitting antennas M TX To calculate, N satisfies:

[0151] |N|<=(M ТХ -1) / 2 (4)

[0152] When the number of transmitting antennas is odd;

[0153] |N|<=(M TX / 2)-1 (5)

[0154] When the number of transmitting antennas is even;

[0155] Use formulas (4) and (5) to get the velocity blur period N, where M TX is the number of transmitting antennas;

[0156] Then, according to the N value, all HPC situations are listed, and the radar data is compensated with each assumed phase compensation value in turn. H satisfies formula (2). Then, FFT operation is performed on each compensated data (2N+1 in total) to obtain the angular power spectrum under each assumed condition;

[0157] H=H -N ,...,H k ,...,H N (6)

[0158] By selecting the peak point in the angular power spectrum, the true speed of each speed representative point is determined, and the speed corresponding to the speed representative point is the true speed of the entire target.

[0159] This application derives the velocity blur cycle N, where M TX is the number of transmitting antennas. Using a 12-transmit and 16-receive antenna array, the velocity ambiguity period is 5;

[0160] Then, according to the N value, all the assumed phase compensation situations are listed, and the radar data is compensated in turn with each assumed phase compensation value. Then, FFT operation is performed on each compensated data to obtain the angular power spectrum under each assumed condition. The assumption of the peak point in the angular power spectrum is selected, and its corresponding speed and the calculated angle are the true values ​​of the representative point.

[0161] The step 4 is specifically as follows:

[0162] It is crucial to calibrate the phase change caused by the target speed through velocity ambiguity resolution technology for the accuracy of radar imaging. After the RDM containing the target speed and distance information is processed by CFAR detection, the estimated value of the target speed and the Doppler phase difference can be obtained.

[0163] However, in actual applications, the speed of moving targets often exceeds the maximum detectable speed of the radar. TX =2,N RX =4) as an example, a virtual antenna array of 1 transmit and 8 receive can be formed. Assume that the result sequence passing through the CFAR target point is X (m , n) (0 <m<N TX ,0 <n<N RX ), then the phase compensation satisfies the formula:

[0164]

[0165] When using angle-dimensional FFT for DOA estimation, the signal energy in each direction will be converted to the frequency domain, so different angles will be mapped to different positions in the frequency domain. When using FFT to process signals, the direction information of the target signal is "mapped" to the frequency domain, and the direction of arrival of the signal is determined by calculating the energy spectrum at different angles;

[0166] The formula is:

[0167]

[0168] Wherein M represents the number of receiving antennas; in this application, based on an antenna array with 12 transmit and 16 receive antennas, M is 16.

[0169] The result of FFT processing is an angle spectrum P(θ), which represents the signal power at different angles. The arrival angle of the target is estimated by finding the peak position of the power spectrum. In the calculated angle spectrum P(θ), the angle corresponding to the peak is the arrival angle of the target. By analyzing these peaks, the arrival angles of multiple target sources can be estimated.

[0170] The step 5 is specifically as follows:

[0171] The improved assumed phase compensation algorithm model for solving velocity ambiguity is as follows: Figure 2 As shown in the figure, the input of the algorithm is the distance index, Doppler index and power amplitude of the target point after CFAR detection. Based on the representative point selection method of the target RDM geometric center point and peak point, the representative points of the three targets are obtained, which are respectively recorded as: representative point 1, representative point 2 and representative point 3. For each representative point, RDM provides its velocity information V cfar1 , V cfar2 , V cfar3 , and phase information The HPC velocity deambiguation algorithm first obtains the assumed velocity ambiguity period N according to the number of radar transmitting antennas, and then enumerates all possible assumed phase compensation (HPC) situations for each representative point based on this period and the known velocity information. Next, each assumed phase compensation value is applied to the target point information, and the angular power spectrum under various assumed conditions is calculated. By selecting the assumption of the peak point in the angular power spectrum, the true speed of each representative point is determined, and the corresponding speed of the representative point is the true speed of the entire target. Based on the velocity ambiguity period corresponding to the peak point (N1, N2 and N3 respectively), the phase compensation value of each target is calculated, and all target points are phase compensated according to their respective deambiguation periods. Then, DOA estimation (such as angle-dimensional FFT, DBF) is performed to accurately obtain the true azimuth angle value of each target. Finally, the true distance of the target is calculated based on the relationship between the target point distance index and the radar distance resolution, and the target distance and angle information are converted into coordinates to obtain the point cloud image of the target.

[0172] The measured effect of the present invention:

[0173] In order to verify the velocity deambiguation method suitable for 4D millimeter-wave radar imaging proposed in the present invention, the accuracy and time complexity of velocity deambiguation and the final stereoscopic imaging effect are evaluated through experimental tests.

[0174] Figure 5 For the experimental test scene diagram, take a small car as an example, the car size is: 4.0m*1.6m*1.4m, the driving speed is about 27km / h, and the lane width is 3.75m. Assume that the radar is located at the left rear side of the target to detect the target.

[0175] Figure 6 To test the effect diagram, the point cloud image size, position and color are used as indicators for evaluation during the experimental analysis. The point cloud color is used to represent the target speed, and the point cloud position is used to represent the target angle. Figure 6 (a) is the point cloud image of the car obtained when the speed ambiguity is not resolved. From the point cloud image, it can be seen that the speed corresponding to the color of the car point cloud is not the same as the actual speed value of the car. In addition, there is a large deviation between the point cloud position of the target and the actual position (blue box). Figure 6 (b) and (c) are the results of the speed deblurring test using the original HPC algorithm. It can be seen from the point cloud image that after deblurring, the speed of most of the target points is close to the actual speed of the target. A small number of points have errors in the target angle estimation due to the deviation in speed estimation, which leads to obvious point cloud discreteness in the final point cloud image. Figure 6 (d) and (e) are the target space stereo point cloud images obtained by using the velocity defuzzification method proposed in the present invention. Figure 6 From the point cloud images of (d) and (e), it can be seen that the speed corresponding to the color of the point cloud image of the car is about -27 km / h, which is basically consistent with the actual speed of the car, and the point cloud image of car 1 is located in the interval [0,3.75], which is the same as the actual position of car 1. Figure 6 (e) is a top view of the three-dimensional point cloud of car 1. It can be seen from the figure that the length of the point cloud image is about 4m and the width is about 1.6m. The overall size of the point cloud image is close to the actual size of car 1. Figure 6 (d) It can be seen that the height of the point cloud image is about 1.6m, which is slightly higher than the actual height of the car 1.4m. This is because the target is expanded in the angle spectrum. Because the target angle spectrum has not only the main lobe, when the side lobe is relatively high, the target angle spectrum will diffuse, causing the target point cloud volume to be larger than the actual volume.

[0176] Figure 7The following is a time complexity comparison chart of the methods. Due to the low range resolution and speed resolution, the commonly used traffic radar usually identifies a small car as about 10 target points. In the MIMO radar imaging system, due to the improvement of range, speed and angle resolution and the short imaging distance, the size of the target can be presented as much as possible. A small car will be identified as about 400 target points. Set the number of target points from 10 to 400, such as Figure 5 The relationship between the number of target points and the algorithm time complexity is intuitively represented. As the number of target points increases, the time complexity of the HPC algorithm shows an upward trend. Compared with the original HPC algorithm, its time complexity is more sensitive to the number of target points, while the improved HPC algorithm is significantly lower than the original HPC algorithm in time complexity, and shows a more gentle upward trend with the increase in the number of target points, showing strong robustness. For MIMO radar imaging systems, because spatial stereo imaging of moving targets is required, the radar has a higher spatial resolution, which causes a target to be parsed into multiple points, increasing the processing complexity. When the original HPC algorithm is used to process high-resolution MIMO radar data, its time complexity increases rapidly with the increase in the number of target points, which will lead to low algorithm efficiency and long processing time in practical applications, especially when the number of target points is large. In contrast, the improved HPC algorithm can more effectively maintain algorithm performance and efficiency when processing MIMO radar data with high resolution. Replacement is suitable for the needs of spatial stereo imaging.

[0177] The key point of the present invention is to design a velocity deambiguation method suitable for 4D millimeter-wave radar, so that the 4D millimeter-wave radar has the ability to detect high-speed targets and optimize the point cloud imaging effect.

[0178] The present invention is applied to intelligent transportation systems. Traditional speed defuzzification algorithms require multiple frames of data and have poor real-time performance, which cannot meet the requirements of intelligent transportation systems. The method proposed in the present invention can improve the spatial stereo imaging capability of millimeter-wave radar traffic moving targets and provide more abundant and intuitive traffic target information for intelligent transportation systems.

[0179] The present invention is applied to radar imaging systems. Radar imaging systems usually have a high spatial resolution, which also leads to a decrease in the maximum unambiguous speed range that can be detected. The method proposed by the present invention is applicable to radar imaging systems and can effectively reduce the time complexity of the speed deambiguation algorithm.

Claims

1. A velocity deambiguation method suitable for 4D millimeter wave radar imaging, characterized in that: The steps include: Step 1: Perform two-dimensional fast Fourier transform processing on the original target sampling data of the 4D millimeter-wave radar to obtain the target range-Doppler matrix; And through constant false alarm detection, the target point distance, target point speed and phase information are obtained; Step 2: Find the best representative point as the velocity representative point of the target, which is located near the midpoint of the line connecting the peak point of the target point cloud image and the geometric center point of the target RDM; Step 3: Use the assumed phase compensation speed defuzzification algorithm for the speed representative point of the target to obtain the speed and phase compensation value corresponding to the speed representative point; Step 4: Select the velocity ambiguity period corresponding to the peak point of the angular power spectrum of the target point under the assumed conditions, and perform DOA estimation on the phase compensation value calculated based on the representative point to obtain the azimuth angle value; Step 5: Based on the obtained target point speed and azimuth angle values, the target real distance is calculated according to the relationship between the target point distance index and the radar distance resolution, and the target distance and angle information are converted into coordinates to obtain the real position of the target in the three-dimensional coordinate system. The speed of the speed representative point represents the real speed of the target.

2. The velocity deambiguation method for 4D millimeter wave radar imaging according to claim 1, characterized in that: The step 1 is specifically as follows: Step (1): perform ADC sampling on the difference frequency signal generated by mixing the echo signals of multiple chirps and the local oscillator signal in each frame of the original target sampling data, and store the sampling results in the form of a matrix, storing each chirp signal sampling result in the matrix row by row; Step (2): performing FFT transformation on each row and each column of the matrix to obtain a target range-Doppler spectrum containing target speed and range information; Step (3): Perform constant false alarm detection on the target RDM to obtain the target point distance, target point speed and phase information.

3. The velocity deambiguation method suitable for 4D millimeter wave radar imaging according to claim 2, characterized in that: The step (2) is specifically: The time delay of the echo is calculated through the spectrum of the difference frequency signal to determine the target distance. The transmitted Chirp signal is expressed by the following formula: S TX =Aexp(j×(2πf c t+πkt 2 )),t∈(0,T c ) Among them, f c is the carrier frequency, T c is the period, A is the amplitude, is the slope of frequency versus time, B is the signal bandwidth, and t is the time coordinate of the signal frequency changing with time within one cycle; The difference frequency signal is generated by mixing the target reflected signal with the reference signal, and is expressed as follows: S IF =Aexp(j×(2π·kτ·t+2π·f c t-πkt 2 )) in is the round-trip delay of the electromagnetic wave, c is the speed of light, R represents the distance between the target and the radar, the frequency of the difference frequency signal is linearly related to the delay τ, and the difference frequency signal is subjected to an FFT to obtain the signal frequency of the difference frequency signal, and the distance R of the target is calculated. The formula is: In digital domain processing, the number of sampling points is N r , the sampling rate is F s Under the sampling condition of , the distance resolution R of the distance information obtained after digital signal processing res , expressed as: For the same moving target, perform FFT on two adjacent difference frequency signals separately to obtain different phase information. The one-dimensional FFT peaks of the two difference frequency signals are the same, but the phases are different. The speed information of the target can be obtained based on this phase difference. The process of using the two-dimensional FFT algorithm to process a frame of difference frequency signal of the target is expressed as: First, the echo signals of multiple chirps in each frame are sampled and the sampling results are stored in the form of a matrix. The sampling results of each chirp signal are stored in the matrix row by row. Then, each row and column of the matrix is ​​transformed by FFT to obtain the range-Doppler spectrum. By analyzing the range-Doppler spectrum (RDM), the distance and speed of the target are estimated.

4. The velocity deambiguation method for 4D millimeter wave radar imaging according to claim 2, characterized in that: The step (3) is specifically as follows: 1) firstly, a threshold is solved for the distance dimension of the measured unit in the RDM matrix. According to the principle of the CACFAR algorithm, the detection threshold of the measured unit in the distance dimension is: Among them, M is the number of training units, rx i ,ry i , are the training units on the left and right of the tested unit, respectively, and K is the threshold factor; 2) Then, the threshold of the unit under test in the velocity dimension of the RDM matrix is ​​solved in the same way as the distance dimension. The threshold of the unit under test in the velocity dimension is dx i , dy are the training units on the upper and lower sides of the tested unit respectively; 3) After calculating the distance dimension and speed dimension thresholds, the unit under test is tested for crossing the threshold. The crossing threshold test can be expressed as (A>T r )&(A>T d ), A is the power of the unit under test in RDM. Only when it exceeds the distance dimension and speed dimension threshold at the same time, it is identified as a target; After CFAR, the distance, velocity and phase information of the target point are obtained; The speed and phase information of the target point are used as the input for subsequent speed deambiguation. The distance of the target point corresponds to the distance index, and the speed of the target point corresponds to the Doppler index. The speed of the target is calculated by the Doppler frequency shift of the target. The phase information and the echo intensity of the target signal are the power amplitude of the target point.

5. The velocity deambiguation method suitable for 4D millimeter wave radar imaging according to claim 1, characterized in that: The step 2 is specifically as follows: First, find the point with the strongest energy in the point cloud image of the target, that is, the peak point, and the midpoint of the line connecting the geometric center point of the target RDM. The geometric center point of the target RDM is obtained by 2D FFT, and then the target area is extracted after target detection, and the geometric center point is calculated. Then, all target points are traversed, and the point closest to this midpoint in RDM is selected as the representative point of the target, and this representative point is used as the speed representative point; the geometric center point of RDM is obtained by RDM calculation, and then the best point is found near the midpoint between the geometric center point of RDM and the peak point.

6. The velocity deambiguation method for 4D millimeter wave radar imaging according to claim 1, characterized in that: In step 3, a velocity defuzzification algorithm using a hypothetical phase compensation velocity is used for the velocity representative point of the target. The method is as follows: First, the detection phase shift of the CFAR detection output of the target point Enumerate all possible phase values ​​that may need to be compensated: Secondly, according to formula (1), the radar data is compensated with each assumed phase compensation value in turn, and the compensated data is expressed as: Among them, S i Represents the signal data in the i-th channel, i starts from 2, Indicates the phase value that needs to be compensated. At the same time, data is extracted from the same index position in each channel RDM to form a channel data set as follows: The value of N depends on the number of transmitting antennas M TX To calculate, N satisfies: |N|<=(M TX -1) / 2 (4) When the number of transmitting antennas is odd; |N|<=(M TX / 2)-1 (5) When the number of transmitting antennas is even; Use formulas (4) and (5) to get the velocity blur period N, where M TX is the number of transmitting antennas; Then, according to the N value, all HPC situations are listed, and the radar data is compensated with each assumed phase compensation value in turn. H satisfies formula (2). Then, FFT operation is performed on each compensated data (2N+1 in total) to obtain the angular power spectrum under each assumed condition; H=H -N ,...,H k ,...,H N (6) By selecting the peak point in the angular power spectrum, the true speed of each speed representative point is determined, and the speed corresponding to the speed representative point is the true speed of the entire target.

7. The velocity deambiguation method for 4D millimeter wave radar imaging according to claim 1, characterized in that: The step 4 is specifically as follows: When the RDM containing the target speed and distance information is processed by CFAR detection, the estimated value of the target speed and the Doppler phase difference are obtained; Set the result sequence passing through the CFAR target point to X (m，n) (0 <m<N TX , 0 <n<N RX ), then the phase compensation satisfies the formula: Where X(m,n) is the original signal matrix, is the phase factor, m is the transmit antenna index, n is the receive antenna index, and N TX is the number of transmit antennas, N RX is the number of receiving antennas; Use angle-dimensional FFT for DOA estimation. When using FFT to process signals, the direction information of the target signal is "mapped" to the frequency domain, and the direction of arrival of the signal is determined by calculating the energy spectrum at different angles. The formula is: Where M represents the number of receiving antennas; The result of FFT processing is an angle spectrum P(θ). The arrival angle of the target is estimated by finding the peak position of the power spectrum. In the calculated angle spectrum P(θ), the angle corresponding to the peak is the arrival angle of the target.

8. The velocity deambiguation method applicable to 4D millimeter wave radar imaging according to claim 1, characterized in that: The step 5 is specifically as follows: The target point distance, speed and phase information are used as the input of the improved hypothetical phase compensation speed ambiguity resolution algorithm model. Based on the representative point selection method of the target RDM geometric center point and peak point, the representative points of the three targets are obtained, which are respectively recorded as: representative point 1, representative point 2 and representative point 3. For each representative point, RDM provides its speed information V cfar1 , V cfar2 , V cfar3 , and phase information The velocity ambiguity resolution algorithm first obtains the assumed velocity ambiguity period N according to the number of radar transmitting antennas, and then enumerates all possible assumed phase compensation situations for each representative point based on this period and the known velocity information; Next, each assumed phase compensation value is applied to the target point information to calculate the angular power spectrum under various assumed conditions. By selecting the assumption of the peak point in the angular power spectrum, the true speed of each representative point is determined. The corresponding speed of the representative point is the true speed of the entire target. Based on the velocity ambiguity periods corresponding to the peak points, which are N1, N2 and N3, the phase compensation value of each target is calculated, and phase compensation is performed on all target points according to their respective deambiguation periods, and then DOA estimation is performed to accurately obtain the true azimuth angle value of each target; Finally, the real distance of the target is calculated according to the relationship between the target point distance index and the radar distance resolution, and the coordinate transformation is performed to form a point cloud image with target distance and angle information.

9. The velocity deambiguation method applicable to 4D millimeter wave radar imaging according to claim 8, characterized in that: Phase information Perform azimuth-elevation FFT to obtain the target's AEM matrix; Afterwards, the AEM is subjected to a constant false alarm detection method based on the normalized zero-point angle power spectrum to obtain the target azimuth and elevation angle information; Finally, the target's distance, azimuth and elevation information are transformed into coordinates to generate a three-dimensional point cloud image of the target space.

10. Application of a velocity deambiguation method suitable for 4D millimeter wave radar imaging according to any one of claims 1 to 9, characterized in that: The method is applied to intelligent transportation systems and radar imaging systems.

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