A velocity ambiguity resolution method based on assumed phase compensation

Abstract

The present invention discloses a method for solving velocity ambiguity by assuming phase compensation, which comprises: identifying and detecting dynamic targets, acquiring radar data, and determining the Doppler phase difference φ corresponding to the dynamic targets. cfar , all channel data sets S and assumed speed ambiguity period N; according to the assumed speed ambiguity period N and the phase compensation formula, perform Doppler phase compensation on the radar data to obtain compensated radar data; draw an angular power spectrum based on the compensated radar data, determine the target peak point according to the peak point in the angular power spectrum, and determine the representative point of the dynamic target according to the target peak point and the geometric center of the dynamic target, as well as the time complexity of the representative point; based on the time complexity corresponding to the representative point and the target speed estimation value V cfar , determine the true speed of the dynamic target V true By applying the method provided in this application, the accuracy and computing performance of speed estimation are improved.

Description

Technical Field

[0001] The present application relates to the technical field of electrical data processing, and more specifically, to a method for solving velocity ambiguity by assuming phase compensation. 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 the target exceeds the maximum frequency of the radar system's processing capability, 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, and incorrect target speed estimation will affect the accuracy of target angle estimation. Therefore, velocity ambiguity resolution is critical for MIMO imaging radar. Methods based on pulse repetition frequency (PRF) changes are usually common solutions to velocity ambiguity problems, including the Chinese Remainder Theorem (CRT) method, table lookup method, and one-dimensional set algorithm.

[0003] 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 velocity. However, in the classical 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. On the other hand, the robust Chinese Remainder Theorem is a search-based algorithm that can guarantee the correct estimation of the Doppler frequency under certain conditions. For example, Yue-bin et al. proposed a new method to resolve the velocity ambiguity of pulse Doppler (PD) radar. This method overcomes the velocity ambiguity problem by using multi-carrier frequency waveforms and the Chinese Remainder Theorem. On this basis, an improved algorithm with better robustness is derived. Xiao et al. proposed a robust phase unwrapping method based on the Chinese Remainder Theorem (CRT) and applied it in moving target synthetic aperture radar (SAR) imaging. This algorithm only performs one-dimensional search, thus greatly reducing the complexity. Both algorithms seek overlap by spreading the signal in different aliasing periods or by algebraic techniques when spreading different prf estimates in different aliasing periods. This concept of searching for overlapping estimates allows the concept of clustering to be applied to solving velocity ambiguities, and an improved clustering algorithm is proposed and compared with an improved Chinese Remainder Theorem (CRT) algorithm. The results show that the algorithm has a lower false alarm probability and can be extended to multiple target and clutter scenarios.

[0004] The classic table lookup method pre-calculates and stores the velocity-Doppler shift relationship under different circumstances to quickly match the actually observed Doppler shift, thereby finding the true velocity of the target. Based on this idea, a simple algorithm for distance ambiguity and velocity ambiguity based on a residual algorithm is proposed. The algorithm uses a residual lookup table method to obtain unambiguous results, and uses another algorithm to solve the special problem of filter bandwidth consistency in frequency. Although this method is simple and easy to implement in engineering, table storage requires a lot of storage space, and in the lookup table, the degrees are divided by distance units, and a lot of time is wasted on unnecessary numerical calculations, making it difficult to meet the requirements of real-time processing and difficult to adapt to complex scenarios.

[0005] In view of the technical problems existing in the existing MIMO radar technology, such as speed ambiguity, difficulty in meeting the requirements of real-time processing, complex processing, and large storage space, it is necessary to introduce an improved hypothetical phase compensation method to simplify the processing flow and complexity of solving the speed ambiguity of the road test multi-channel MIMO millimeter-wave radar hypothetical phase compensation according to the influencing factors of the speed ambiguity problem on the angle estimation, reduce the number of target points that the algorithm needs to process, and ensure the accuracy of speed estimation, accelerate the processing flow of obtaining the target speed and angle in the MIMO radar imaging processing, reduce abnormal speed points, and thus improve the accuracy and calculation performance of speed estimation. Summary of the invention

[0006] In response to the technical problems mentioned above, the present invention provides a method for resolving speed ambiguity by assuming phase compensation. According to the influencing factors of the speed ambiguity problem on angle estimation, by providing and applying a method for reducing the number of target points for dynamic target detection, and an improved algorithm formula for resolving speed ambiguity by assuming phase compensation, the technical problems existing in the prior art such as MIMO radar speed ambiguity, difficulty in meeting the requirements of real-time processing, complex processing and large data storage space are solved. The processing flow and complexity of resolving speed ambiguity by assuming phase compensation for road test multi-channel MIMO millimeter-wave radar are simplified, the number of target points that the algorithm needs to process is reduced, and the accuracy of speed estimation is ensured, the processing flow of obtaining target speed and angle in MIMO radar imaging processing is accelerated, abnormal speed points are reduced, and the real-time, correctness and computing performance of speed estimation are improved.

[0007] The present invention provides a method for solving velocity ambiguity by assuming phase compensation, the method comprising:

[0008] S1, defining a phase compensation data structure according to the influencing parameters of the assumed phase compensation solution to the velocity ambiguity; S2, identifying and detecting the dynamic target based on the MIMO millimeter wave radar, the constant false alarm rate detector and the phase compensation data structure, obtaining radar data and the Doppler phase difference corresponding to the dynamic target , all channel data sets S and assumed speed ambiguity period N; S3, according to the assumed speed ambiguity period N and the phase compensation formula, perform Doppler phase compensation on the radar data to obtain compensated radar data; S4, draw an angular power spectrum based on the compensated radar data, determine the target peak point according to the peak point in the angular power spectrum, and determine the representative point of the dynamic target according to the target peak point and the geometric center of the dynamic target, as well as the time complexity of the representative point; S5, based on the time complexity corresponding to the representative point and the target speed estimation value V cfar , determine the true speed of the dynamic target V true , and the complexity is reduced by a factor M.

[0009] Preferably, in step S1: the phase compensation data structure includes: the number of transmitting antennas , Number of receiving antennas , target distance and dynamic target data; wherein the dynamic target data includes: target point, number of target points n, representative point, number of representative points a, true speed V true , Phase compensation value , the number of phase compensation values ​​m, the target speed estimate V cfar , Doppler phase difference , Maximum speed V max .

[0010] Preferably, the step S2 includes: S21, according to the number of transmitting antennas and the number of receiving antennas of the MIMO millimeter wave radar, and the target point of the dynamic target, obtaining a channel data set of each target point detected by the MIMO millimeter wave radar and the constant false alarm rate detector , true phase compensation value and a detection result sequence, and an assumed velocity ambiguity period N; S22, according to the assumed velocity ambiguity period N and the true phase compensation value , determine the Doppler phase difference of each target point ; Wherein, the test result is , , is the number of transmitting antennas of the MIMO millimeter-wave radar, is the number of receiving antennas of the MIMO millimeter-wave radar; the Doppler phase difference of the target point , is the actual phase compensation value of each target point , N is the assumed velocity ambiguity period, .

[0011] Preferably, the step S2 further includes: detecting the channel of each target point based on the MIMO millimeter wave radar, and acquiring the channel data set of each target point , and based on the channel data set of each of the target points and Doppler phase difference , determine the phase value that needs to be compensated ;in, Characterization i The radar data corresponding to the target point obtained in the channel, i is the channel number corresponding to each target point, and i is an integer greater than 1; the phase value of the target point that needs to be compensated .

[0012] Preferably, the step S2 further includes: all channel data sets ; Assume that the velocity ambiguity period N is determined according to the number of transmitting antennas of the MIMO millimeter-wave radar, wherein, when the number of transmitting antennas of the MIMO millimeter-wave radar is an odd number, , when the number of transmitting antennas of the MIMO millimeter-wave radar is an even number, The radar data is all channel data in the original format obtained by the channels corresponding to each target point of the dynamic target detected by the MIMO millimeter wave radar, including target point, number of target points n, representative point, number of representative points a, true speed V true , Phase compensation value , the number of phase compensation values ​​m, the target speed estimate V cfar , Doppler phase difference and maximum speed V max .

[0013] Preferably, the step S3 includes: detecting a channel data set of each target point based on the MIMO millimeter wave radar , and assuming the velocity ambiguity period N and the phase compensation formula to perform Doppler compensation calculation on the radar data, to obtain the compensated radar data corresponding to each of the target points; wherein the compensation formula is , is the compensated radar data corresponding to each of the target points, is the phase value that needs to be compensated for the target point, i is the channel number corresponding to each target point, i and j All are greater than 1 and less than or equal to integer, is the number of transmitting antennas of the MIMO millimeter-wave radar.

[0014] Preferably, in step S4, the step of drawing an angular power spectrum based on the compensated radar data, determining a target peak point according to a peak point in the angular power spectrum, and determining a representative point of the dynamic target according to the target peak point and the geometric center of the dynamic target further includes: S41, based on the compensated radar data, according to the assumed velocity ambiguity period N, and the real phase compensation value corresponding to each target point of the dynamic target , draw the angular power spectrum; S42, determine the target peak point and target peak value corresponding to each target point from the peak point in the curve of the angular power spectrum, and sort the target peaks to determine the maximum peak point and the maximum peak value, as well as the time complexity of the maximum peak point, wherein the time complexity of the maximum peak point is , n is the number of target points, m is the number of phase compensation values; S43, determining the representative point of the dynamic target according to the target peak point and the geometric center of the dynamic target, and determining the time complexity of the representative point according to the time complexity of the maximum peak point.

[0015] Preferably, the step S43 further comprises: connecting the target peak point and the geometric center of the dynamic target, and determining the center point and time complexity of the connecting line, determining the representative point according to the distance between the target peak point and the center point, wherein the representative point is a target peak point whose distance from the center point is less than a preset value, and the preset value is set according to the target peak point near the center point; determining the time complexity of the representative point according to the number of representative points of the representative point and the time complexity of the maximum peak point; wherein the time complexity of the representative point is , a is the number of representative points of the representative points, and m is the number of phase compensation values.

[0016] Preferably, the step S5 further comprises: S51, according to the time complexity of the center point of the target peak point and the geometric center line of the dynamic target, the time complexity of the representative point is improved and optimized to obtain the assumed phase compensation speed ambiguity solution algorithm formula and the complexity reduction multiple M, wherein the assumed phase compensation speed ambiguity solution algorithm formula is: , the complexity is reduced by a factor of , n is the number of target points, m is the number of phase compensation values; S52, based on the assumed phase compensation speed ambiguity resolution algorithm formula and the target speed estimation value V cfar Determine the true speed of the dynamic targetV true , where the true speed of the dynamic target is , is the estimated target speed, is the maximum speed of the dynamic target, and N is the assumed speed fuzzy period.

[0017] The present invention realizes the influencing factors of angle estimation according to the speed ambiguity problem by applying the above technical scheme, and solves the technical problems of MIMO radar speed ambiguity, difficulty in meeting the requirements of real-time processing, complex processing and large data storage space existing in the prior art by providing and applying the target points for reducing dynamic target detection and an improved assumed phase compensation speed ambiguity resolution algorithm formula, simplifies the processing flow and complexity of speed ambiguity resolution of multi-channel MIMO millimeter-wave radar assumed phase compensation for road test, reduces the number of target points that need to be processed by the algorithm, and ensures the accuracy of speed estimation, accelerates the processing flow of obtaining target speed and angle in MIMO radar imaging processing, reduces abnormal speed points, and thus improves the real-time, correctness and computing performance of speed estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A schematic diagram of a flow chart of a method for resolving velocity ambiguity by assuming phase compensation proposed in an embodiment of the present invention is shown;

[0020] Figure 2 Schematic diagram showing three phase compensation assumptions;

[0021] Figure 3 A schematic diagram of a curve showing the power amplitude of three hypothetical signals in the angular power spectrum;

[0022] Figure 4 A schematic diagram of the process of selecting a peak point as a target representative point is shown;

[0023] Figure 5 A two-dimensional schematic diagram showing the selection of a geometric center point as a target representative point;

[0024] Figure 6 A schematic diagram showing a representative point selection method combining a peak point with an RDM geometric center point is shown;

[0025] Figure 7The time complexity comparison of the HPC algorithm before and after improvement under different target points is shown;

[0026] Figure 8 A two-dimensional schematic diagram of the original HPC algorithm defuzzification result of a moving target is shown;

[0027] Fig. 9 A two-dimensional schematic diagram of the defuzzification results of the improved HPC algorithm for moving targets is shown. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] The present invention provides a method for solving velocity ambiguity by assuming phase compensation, such as Figures 1 to 9 As shown, the method comprises the following steps:

[0030] S1, define the phase compensation data structure according to the influencing parameters of the assumed phase compensation solution to velocity ambiguity.

[0031] In this embodiment, in step S1: the phase compensation data structure includes: the number of transmitting antennas , Number of receiving antennas , target distance and dynamic target data;

[0032] in,

[0033] The dynamic target data includes: target point, number of target points n, representative point, number of representative points a, true speed V true , Phase compensation value , the number of phase compensation values ​​m, the target speed estimate V cfar , Doppler phase difference , Maximum speed V max .

[0034] S2, based on the MIMO millimeter wave radar, the constant false alarm rate detector and the phase compensation data structure, the dynamic target is identified and detected, and the radar data and the Doppler phase difference corresponding to the dynamic target are obtained. , all channel data sets S and assumed velocity ambiguity period N.

[0035] In this embodiment, the step S2 includes:

[0036] S21, according to the number of transmitting antennas and the number of receiving antennas of the MIMO millimeter wave radar, and the target point of the dynamic target, obtaining a channel data set of each target point detected by the MIMO millimeter wave radar and the constant false alarm rate detector , true phase compensation value and the detection result sequence, and the assumed velocity ambiguity period N;

[0037] S22, based on the assumed velocity ambiguity period N and the true phase compensation value , determine the Doppler phase difference of each target point ;

[0038] in,

[0039] The test results are , , is the number of transmitting antennas of the MIMO millimeter-wave radar, is the number of receiving antennas of the MIMO millimeter-wave radar;

[0040] The Doppler phase difference of the target point , is the actual phase compensation value of each target point , N is the assumed velocity ambiguity period, .

[0041] In this embodiment, the step S2 further includes:

[0042] Detect the channel of each target point based on the MIMO millimeter wave radar, and obtain the channel data set of each target point , and based on the channel data set of each of the target points and Doppler phase difference , determine the phase value that needs to be compensated ;

[0043] in,

[0044] Characterization i The radar data corresponding to the target point obtained in the channel, i is the channel number corresponding to each target point, and i is an integer greater than 1;

[0045] The phase value of the target point that needs to be compensated .

[0046] In this embodiment, the step S2 further includes:

[0047] All channel data sets ;

[0048] Assume that the velocity ambiguity period N is determined according to the number of transmitting antennas of the MIMO millimeter-wave radar, wherein, when the number of transmitting antennas of the MIMO millimeter-wave radar is an odd number, , when the number of transmitting antennas of the MIMO millimeter-wave radar is an even number, ;

[0049] The radar data is all channel data in the original format obtained based on the channels corresponding to each target point of the dynamic target detected by the MIMO millimeter wave radar, including target points, the number of target points n, representative points, the number of representative points a, and the true speed. V true , Phase compensation value , the number of phase compensation values ​​m, the target speed estimate V cfar , Doppler phase difference and maximum speed V max .

[0050] S3, performing Doppler phase compensation on the radar data according to an assumed velocity ambiguity period N and a phase compensation formula to obtain compensated radar data.

[0051] In this embodiment, the step S3 includes:

[0052] Channel data set based on the MIMO millimeter wave radar to detect each target point , and assuming a velocity ambiguity period N and a phase compensation formula to perform Doppler compensation calculation on the radar data, to obtain compensated radar data corresponding to each of the target points;

[0053] in,

[0054] The compensation formula is: , is the compensated radar data corresponding to each of the target points, is the phase value that needs to be compensated for the target point, i is the channel number corresponding to each target point, i and j All are greater than 1 and less than or equal to integer, is the number of transmitting antennas of the MIMO millimeter-wave radar.

[0055] S4, drawing an angular power spectrum based on the compensated radar data, determining the target peak point according to the peak point in the angular power spectrum, and determining the representative point of the dynamic target and the time complexity of the representative point according to the target peak point and the geometric center of the dynamic target.

[0056] In this embodiment, in step S4, the step of drawing an angular power spectrum based on the compensated radar data, determining a target peak point according to a peak point in the angular power spectrum, and determining a representative point of the dynamic target according to the target peak point and the geometric center of the dynamic target further includes:

[0057] S41, based on the compensated radar data, according to the assumed velocity ambiguity period N and the actual phase compensation value corresponding to each target point of the dynamic target , plot the angular power spectrum;

[0058] S42, determining the target peak point and target peak value corresponding to each target point from the peak point in the curve of the angular power spectrum, and sorting the target peaks to determine the maximum peak point and the maximum peak value, as well as the time complexity of the maximum peak point, wherein the time complexity of the maximum peak point is , n is the number of target points, m is the number of phase compensation values;

[0059] S43, determining a representative point of the dynamic target according to the target peak point and the geometric center of the dynamic target, and determining the time complexity of the representative point according to the time complexity of the maximum peak point.

[0060] In this embodiment, the step S43 further includes:

[0061] Connect the target peak point and the geometric center of the dynamic target, determine the center point of the connection and the time complexity, and determine the representative point according to the distance between the target peak point and the center point, wherein the representative point is a target peak point whose distance from the center point is less than a preset value, and the preset value is set according to the target peak point near the center point;

[0062] Determining the time complexity of the representative point according to the number of representative points of the representative point and the time complexity of the maximum peak point;

[0063] Among them, the time complexity of the representative point is , a is the number of representative points of the representative points, and m is the number of phase compensation values.

[0064] S5, based on the time complexity corresponding to the representative point and the target speed estimation value V cfar , determine the true speed of the dynamic targetV true , and the complexity is reduced by a factor M.

[0065] In this embodiment, the step S5 further includes:

[0066] S51, according to the time complexity of the center point of the target peak point and the geometric center line of the dynamic target, the time complexity of the representative point is improved and optimized to obtain the assumed phase compensation speed ambiguity solution algorithm formula and the complexity reduction multiple M, wherein the assumed phase compensation speed ambiguity solution algorithm formula is: , the complexity is reduced by a factor of , n is the number of target points, m is the number of phase compensation values;

[0067] S52, based on the assumed phase compensation speed ambiguity resolution algorithm formula and the target speed estimation value V cfar Determine the true speed of the dynamic target V true ,in,

[0068] The true speed of the dynamic target , is the estimated target speed, is the maximum speed of the dynamic target, and N is the assumed speed fuzzy period.

[0069] By applying the above technical solutions, the influencing factors of the speed ambiguity problem on the angle estimation are realized. By providing and applying the number of target points for dynamic target detection and the improved assumed phase compensation speed ambiguity resolution algorithm formula, the technical problems existing in the prior art such as MIMO radar speed ambiguity, difficulty in meeting the requirements of real-time processing, complex processing and large data storage space are solved. The processing flow and complexity of speed ambiguity resolution of road test multi-channel MIMO millimeter-wave radar assuming phase compensation are simplified, the number of target points that the algorithm needs to process is reduced, and the accuracy of speed estimation is guaranteed. The processing flow of obtaining target speed and angle in MIMO radar imaging processing is accelerated, and abnormal speed points are reduced, thereby improving the real-time, correctness and computing performance of speed estimation.

[0070] In order to help those skilled in the art better understand the method for resolving velocity ambiguity by assuming phase compensation provided by the present invention, the above steps are further described with examples.

[0071] Step 1: Analyze the impact of velocity blur on angle estimation

[0072] According to the moving target echo signal model received by the radar, the target's velocity parameter This will cause the radar echo signal frequency to produce a Doppler shift , which will produce a phase difference , .

[0073] According to the radar angle measurement principle, the angle estimation , that is, the radar angle determination mainly depends on the phase difference, and the moving target phase difference is related to the Doppler frequency shift That is, there is a coupling relationship between the angle estimation and the speed of the moving target. The ambiguity of the target speed will make the angle estimation value wrong, which will cause the target position of the final point cloud image to shift.

[0074] It is crucial to calibrate the phase change caused by the target speed through velocity ambiguity resolution technology for the accuracy of radar imaging. When the radar range-Doppler map (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. However, in actual applications, the speed of moving targets often exceeds the maximum detectable speed of the radar. Taking the MIMO radar as an example, a virtual antenna array with 1 transmit and 8 receive can be formed. Assume that the result sequence passing through the CFAR target point is , then the phase compensation satisfies the formula: .

[0075] Only knowing the target's true speed Only then can we get the true phase compensation value Assume that the target velocity estimate after CFAR processing is , the Doppler phase difference is . Target true speed With target speed estimate and the actual Doppler phase difference that needs to be compensated The relationship between them is:

[0076] .

[0077] in is the speed blur period. Only when the target speed is less than the maximum unambiguous speed , otherwise, the phase difference is Integer multiple.

[0078] According to the above formula, it can be seen that there is a multiple relationship between the target's true speed and the speed error, that is, the speed ambiguity period. The greater the target speed, the greater the difference between the target speed detected by the constant false alarm rate detector (CFAR) and the actual target speed, which will cause the radar to estimate the target angle. In addition, the difference between the target speed detected by the constant false alarm rate detector (CFAR) and the actual target speed continues to increase. In an urban traffic environment, moving targets are mostly high-speed vehicles. Therefore, the accuracy of target angle measurement depends on the effective solution to speed ambiguity.

[0079] Step 2: Improved hypothetical phase compensation method to resolve velocity ambiguity

[0080] In step 1, the impact of velocity ambiguity on angle estimation is analyzed. It can be seen that when the MIMO radar imaging system performs velocity ambiguity processing on a moving target, the target generally exhibits the characteristics of a large velocity ambiguity period. In order to solve the impact of velocity ambiguity on angle estimation, this application introduces a hypothetical phase compensation (HPC) velocity ambiguity resolution algorithm, which can estimate the true speed of the target under large velocity ambiguity periods and perform velocity interpretation and ambiguity processing within a single frame. Next, the principle of the HPC algorithm is introduced.

[0081] In a MIMO radar system with 4Tx, the phase difference between chirp1 and chirp5 represents the detected target Doppler shift ,when When , velocity ambiguity appears as cyclic Doppler phase aliasing. Figure 2 The leftmost column shows an aliasing situation where three different speeds have the same detected phase shift. , as shown by the red track in the true phase circle of each speed region (R1, R2 and R3). R1 ​​represents a target with a speed less than the maximum unambiguous speed of the radar, R2 represents a target moving away from the radar at a speed greater than the maximum unambiguous speed of the radar, and R3 represents a target approaching the radar at a speed greater than the maximum unambiguous speed of the radar. The true phase shift of R2 and R3 is So we can use HPC to establish three assumptions, extending the explicit maximum unambiguous speed to First, according to the detection phase shift of the CFAR detection output , enumerate all possible phase values ​​that may need to be compensated:

[0082] .

[0083] Secondly, according to the formula , use each assumed phase compensation value to compensate the radar data in turn, and the compensated data is expressed as: ,in, Indicates Signal data in channels, Starting from 2, since only non-initial codes require Doppler phase compensation, Indicates the phase value that needs to be compensated. Note that the number of assumptions cannot be increased infinitely, it is affected by the number of transmitting antennas in the system. Ideally, the maximum detection speed of the HPC algorithm is In the R1 case, the target speed is within the maximum unambiguous speed range detectable by the radar, and the compensation phase is In the case of and Additional phase compensation Compensation, the phase not only converges to In Compensation, when the speed is and hour, and Phase focusing occurs. Finally, after the angle dimension FFT, the angular power spectrum under each assumption is obtained, where the assumption of the peak point is correct, and the corresponding speed and angle are the target real speed and angle.

[0084] like Figure 3 As shown in the figure, three hypotheses are given for the signal amplitude of the next target point on the angle power spectrum. , and exist The peak value appears around The peak value is the highest, indicating that the actual speed is .

[0085] Step 3: Representative point selection based on geometric center and peak position

[0086] The processing flow of using HPC velocity ambiguity resolution algorithm to solve velocity ambiguity problem and obtain the target's true velocity and angle is as follows:

[0087] Step 1): Before implementing the algorithm, you need to determine the detection phase , channel dataset And assume the velocity blur period The first CFAR detection of the target data collected by the radar can obtain the target range and Doppler information in the radar range-Doppler map (RDM) and calculate the detection phase At the same time, data is extracted from the same index position in each channel radar range-Doppler map (RDM) to form a channel data set: , Value depends on the number of transmitting antennas To calculate. Among them, Satisfies: When the number of transmitting antennas is odd, , when the number of transmitting antennas is even, .

[0088] Step 1): According to Value, list all HPC cases, and use each assumed phase compensation value to compensate the radar data in turn. Satisfied . Then for each compensation data (a total of ) to perform FFT operation and obtain the angular power spectrum under various assumptions. .

[0089] Step 3): Select the assumption that the peak point in the angular power spectrum corresponds to the speed and angle obtained, which are the true values ​​of the target.

[0090] The HPC velocity deambiguation algorithm needs to perform the above processing for each target point detected by CFAR. The time complexity of listing all HPC cases for a target point depends on the number of assumed phase compensation values, which is set to After performing FFT operation on all the compensation values ​​to obtain the angular power spectrum, it is necessary to quickly sort the peak values ​​after all the assumed phase compensation. The time complexity of selecting the maximum peak point is: , then the overall time complexity of this step is: Assume that a moving target is detected by the MIMO radar as target points, CFAR detection can usually be considered as linear time complexity, that is, ,in is the number of target points. That is, the time complexity of the entire HPC velocity fuzzy solution process is: ,in, The value is related to the number of transmitting antennas. Therefore, the time complexity of the HPC velocity ambiguity resolution algorithm is mainly affected by the number of target points. impact.

[0091] MIMO radar 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 ambiguity elimination 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, thereby generating noise points in the point cloud data. Because these target points actually represent the same target, if the HPC velocity ambiguity elimination algorithm is selected, the time complexity of the algorithm will be significantly increased, thereby reducing the system processing speed. In addition, the higher energy of the sidelobe points may cause velocity expansion, thereby generating noise points in the point cloud data. Because these target points actually represent the same target, if the HPC velocity ambiguity elimination algorithm is applied to each target point, the time complexity of the algorithm will be significantly increased, thereby reducing the system processing speed. In addition ... The HPC velocity defuzzification algorithm is used for each representative point. Its velocity value represents the overall velocity value, and its phase value that needs to be compensated represents the phase value that needs to be compensated for all points, then the time complexity of the HPC velocity ambiguity solution process is: .

[0092] This method significantly reduces the time complexity of the HPC speed defuzzification process and improves the system processing efficiency.

[0093] There are two ways to select representative points. First, you can select the point with the strongest energy in the target point cloud image, that is, the peak point as the representative point, but this method has limitations. Although the peak point as the highest energy point of the target does represent the strongest reflection position of the target, this position is not always consistent with the geometric center of the target. Figure 4 As shown in the figure, in traffic scenes, radars are generally installed on both sides of the road. When the peak point is selected as the representative point, the relative azimuth between the target and the radar is obtained as: , and the actual angle value is , there is an angle error: It can be seen that representing the entire target only by the point with the highest reflection energy may lead to errors in angle estimation, which may eventually be reflected as target position offset in the imaging results, affecting the imaging quality and the accuracy of target positioning.

[0094] On the other hand, another approach is to select representative points based on the geometric center of the target in the radar range-Doppler map (RDM). Figure 5 As shown in the figure, the advantage of the geometric center point as a representative point is that it can provide a point that is more representative of 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 point represents the geometric center point obtained from the target radar range-Doppler map (RDM) analysis. However, 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 a representative point, it may introduce errors in the velocity ambiguity resolution process, affecting the accuracy of phase compensation and thus affecting the imaging results.

[0095] Selecting the peak point as the representative point can reflect the target's highest energy point, but may not reflect the target's exact location. Selecting the target's radar range-Doppler map (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. Therefore, this application proposes a representative point selection strategy to combine the advantages of these two methods. Figure 6As shown in the figure, the red dot represents the actual geometric center of the target. Since the geometric center of different moving targets is different, for example, a car has a geometric center inside the car, which cannot be directly detected by the radar. Therefore, the strategy adopted by this application is to find an optimal representative point, which is located near the midpoint of the line connecting the target peak point and the geometric center of the target radar range-Doppler map (RDM), and use this point as the representative point.

[0096] Step 4: Algorithm performance simulation and comparative analysis

[0097] This section verifies the effectiveness of the proposed improved hypothetical phase compensation velocity ambiguity resolution algorithm, specifically from two dimensions: time complexity and accuracy of target velocity ambiguity resolution.

[0098] First, verify the time complexity, assuming that the time complexity of the phase compensation speed ambiguity solution algorithm is: ,in is the target number, is the number of assumed phase compensation values, which is 5 for a 12-transmit 16-receive MIMO radar. The time complexity of the improved assumed phase compensation velocity ambiguity resolution algorithm is calculated below. For a The time complexity of finding the peak point of the target and the geometric center of the radar range-Doppler map (RDM) is , and then the time complexity of calculating the center point between two points is: Finally, the time complexity of finding the point closest to the target point, that is, the representative point, is: , combining the above steps, the total computational complexity is mainly composed of traversing The total complexity is determined by the operations of the points. , the time complexity of HPC processing on the representative points is: That is, the time complexity of the improved hypothetical phase compensation velocity ambiguity resolution algorithm is: , it is assumed that the time complexity of the phase compensation velocity deambiguation algorithm before and after the improvement satisfies: ,in, The algorithm complexity is reduced by a factor of 2.

[0099] Because small cars are the most common moving targets in traffic scenes, this application uses small cars as an example to verify the algorithm. Assuming that the length of the car is about 4.5m, commonly used traffic radars will usually identify small cars as about 10 target points due to their low distance resolution and speed resolution. In the MIMO radar imaging system, due to the improvement of distance, speed and angle resolution and the shorter 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 7 As shown, the relationship between the target number of points and the algorithm time complexity is intuitively expressed. The time complexity reduction factor satisfies the formula: . As the number of target points increases, the time complexity of the HPC algorithm increases. 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 in time complexity than the original HPC algorithm, and shows a more gentle upward trend with the increase in the number of target points, showing strong robustness. For MIMO radar imaging systems, in order to achieve spatial stereoscopic imaging of moving targets, 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 leads 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 stereoscopic imaging.

[0100] by Figure 8 and Fig. 9 Taking the dynamic target shown in the figure as an example, assuming that the radar is located at the left rear side of the target to detect the target, and the actual speed of the car is about 25km / h. First, the accuracy of the improved HPC algorithm in resolving target speed ambiguity is verified through the two-dimensional point cloud image of the target. Figure 8 , is the vehicle point cloud image obtained by using the original HPC speed deambiguation algorithm. From the point cloud image, it can be seen that the speed value after deambiguation using the original HPC algorithm has errors, resulting in a relatively scattered point cloud and a significant deviation between the point cloud image and the actual position of the target. This is because the original HPC algorithm needs to cite all possible hypothetical phase compensation values ​​for all target points when deambiguating speed. In the MIMO radar system, due to the sidelobe effect and the limitation of speed resolution, the target energy will expand in the distance dimension and the Doppler dimension, which will cause errors in the speed after deambiguation. These errors will in turn affect the accuracy of the phase compensation, and ultimately cause the target position to be far away from the actual position. Fig. 9 , is the vehicle point cloud image obtained by using the improved HPC speed defuzzification algorithm. The target speed is defined by selecting the representative point speed and performing phase compensation on the target. This processing method makes the target speed basically consistent with the actual speed and the position is close to the actual position. According to Table 1, the improved HPC speed defuzzification algorithm has a shorter running time and the speed value after defuzzification is closer to the actual speed of the car.

[0101] Table 1 Comparison of velocity ambiguity resolution results of HPC algorithm before and after improvement of moving target

[0102] Calculation time of the original HPC algorithm defuzzification results 128s Calculation time of defuzzification results using improved HPC algorithm 45s Average speed of defuzzified point cloud using the original HPC algorithm 14.4km / h Average speed of defuzzified point cloud using improved HPC algorithm 24.5km / h

[0103] By applying the above technical solutions, this application studies the speed ambiguity problem of MIMO millimeter-wave radar system and the hypothetical phase compensation (HPC) speed ambiguity resolution algorithm. First, the causes of the speed ambiguity problem and its impact on the target angle estimation are discussed. Subsequently, the basic principles and algorithm implementation process of the hypothetical phase compensation speed ambiguity resolution algorithm are explained, and the original HPC algorithm is explained through theoretical analysis. The problem of high time complexity in the application of the MIMO radar system. Finally, an improved hypothetical phase compensation speed ambiguity resolution algorithm is proposed. The algorithm obtains target characteristics based on the MIMO radar, and proposes a method of selecting target representative points by combining geometric center points with peak points. This method can significantly reduce the number of target points that the algorithm needs to process, and ensure the accuracy of speed estimation, accelerate the processing flow of obtaining target speed and angle in MIMO radar imaging processing, and reduce abnormal speed points. The simulation results show that compared with the original HPC speed ambiguity resolution algorithm, the improved HPC speed ambiguity resolution algorithm has lower time complexity and has accurate speed estimation capabilities.

[0104] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A method for resolving velocity ambiguity by assuming phase compensation, characterized in that: The method comprises: S1, defining a phase compensation data structure according to the influencing parameters of the assumed phase compensation solution velocity ambiguity; S2, based on the MIMO millimeter wave radar, the constant false alarm rate detector and the phase compensation data structure, the dynamic target is identified and detected, and the radar data and the Doppler phase difference corresponding to the dynamic target are obtained. , all channel data sets S and assumed velocity ambiguity period N; S3, performing Doppler phase compensation on the radar data according to an assumed velocity ambiguity period N and a phase compensation formula to obtain compensated radar data; S4, drawing an angular power spectrum based on the compensated radar data, determining a target peak point according to a peak point in the angular power spectrum, and determining a representative point of the dynamic target and a time complexity of the representative point according to the target peak point and a geometric center of the dynamic target; S5, based on the time complexity corresponding to the representative point and the target speed estimation value V cfar , determine the true speed of the dynamic target V true , and the complexity reduction factor M; The step S2 includes: S21, according to the number of transmitting antennas and receiving antennas of the MIMO millimeter wave radar, and the target point of the dynamic target, obtaining a channel data set of each target point detected by the MIMO millimeter wave radar and the constant false alarm rate detector , true phase compensation value and the detection result sequence, and the assumed velocity ambiguity period N; S22, based on the assumed velocity ambiguity period N and the true phase compensation value , determine the Doppler phase difference of each target point ; in, The test results are , , is the number of transmitting antennas of the MIMO millimeter-wave radar, is the number of receiving antennas of the MIMO millimeter-wave radar; The Doppler phase difference of the target point , is the actual phase compensation value of each target point , N is the assumed velocity ambiguity period, ; The step S3 includes: Channel data set based on the MIMO millimeter wave radar to detect each target point , and assuming a velocity ambiguity period N and a phase compensation formula to perform Doppler compensation calculation on the radar data, to obtain compensated radar data corresponding to each of the target points; in, The compensation formula is: , is the compensated radar data corresponding to each of the target points, is the phase value that needs to be compensated for the target point, i is the channel number corresponding to each target point, i and j All are greater than 1 and less than or equal to integer, is the number of transmitting antennas of the MIMO millimeter-wave radar.

2. The method according to claim 1, characterized in that In step S1, the phase compensation data structure includes: the number of transmitting antennas , Number of receiving antennas , target distance and dynamic target data; in, The dynamic target data includes: target point, number of target points n, representative point, number of representative points a, true speed V true , Phase compensation value , the number of phase compensation values ​​m, the target speed estimate V cfar , Doppler phase difference , Maximum speed V max .

3. The method according to claim 1, characterized in that The step of S2 further includes: Detect the channel of each target point based on the MIMO millimeter wave radar, and obtain the channel data set of each target point , and based on the channel data set of each of the target points and Doppler phase difference , determine the phase value that needs to be compensated ; in, Characterization i The radar data corresponding to the target point obtained in the channel, i is the channel number corresponding to each target point, and i is an integer greater than 1; The phase value of the target point that needs to be compensated .

4. The method according to claim 1, characterized in that The step of S2 further includes: All channel data sets ; Assume that the velocity ambiguity period N is determined according to the number of transmitting antennas of the MIMO millimeter-wave radar, wherein, when the number of transmitting antennas of the MIMO millimeter-wave radar is an odd number, , when the number of transmitting antennas of the MIMO millimeter-wave radar is an even number, ; The radar data is all channel data in the original format obtained based on the channels corresponding to each target point of the dynamic target detected by the MIMO millimeter wave radar, including target points, the number of target points n, representative points, the number of representative points a, and the true speed. V true , Phase compensation value , the number of phase compensation values ​​m, the target speed estimate V cfar , Doppler phase difference and maximum speed V max .

5. The method according to claim 1, characterized in that In step S4, the step of drawing an angular power spectrum based on the compensated radar data, determining a target peak point according to a peak point in the angular power spectrum, and determining a representative point of the dynamic target according to the target peak point and the geometric center of the dynamic target further includes: S41, based on the compensated radar data, according to the assumed velocity ambiguity period N and the actual phase compensation value corresponding to each target point of the dynamic target , plot the angular power spectrum; S42, determining the target peak point and target peak value corresponding to each target point from the peak point in the curve of the angular power spectrum, and sorting the target peaks to determine the maximum peak point and the maximum peak value, as well as the time complexity of the maximum peak point, wherein the time complexity of the maximum peak point is , n is the number of target points, m is the number of phase compensation values; S43, determining a representative point of the dynamic target according to the target peak point and the geometric center of the dynamic target, and determining the time complexity of the representative point according to the time complexity of the maximum peak point.

6. The method according to claim 5, characterized in that The step of S43 further comprises: Connect the target peak point and the geometric center of the dynamic target, determine the center point of the connection and the time complexity, and determine the representative point according to the distance between the target peak point and the center point, wherein the representative point is a target peak point whose distance from the center point is less than a preset value, and the preset value is set according to the target peak point near the center point; Determining the time complexity of the representative point according to the number of representative points of the representative point and the time complexity of the maximum peak point; Among them, the time complexity of the representative point is , a is the number of representative points of the representative points, and m is the number of phase compensation values.

7. The method according to claim 1, characterized in that The step of S5 further comprises: S51, according to the time complexity of the center point of the target peak point and the geometric center line of the dynamic target, the time complexity of the representative point is improved and optimized to obtain the assumed phase compensation speed ambiguity solution algorithm formula and the complexity reduction multiple M, wherein the assumed phase compensation speed ambiguity solution algorithm formula is: , the complexity is reduced by a factor of , n is the number of target points, m is the number of phase compensation values; S52, based on the assumed phase compensation speed ambiguity resolution algorithm formula and the target speed estimation value V cfar Determine the true speed of the dynamic target V true ,in, The true speed of the dynamic target , is the estimated target speed, is the maximum speed of the dynamic target, is the assumed velocity blur period.

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