Method for resolving velocity ambiguity by using hypothetical phase compensation

Abstract

Disclosed is a method for resolving velocity ambiguity by using hypothetical phase compensation. The method comprises: identifying and detecting a dynamic target, and acquiring radar data, as well as a Doppler phase difference φ cfar corresponding to the dynamic target, a full-channel dataset S, and a hypothetical velocity ambiguity period N; on the basis of the hypothetical velocity ambiguity period N and a phase compensation formula, performing Doppler phase compensation on the radar data, and obtaining compensated radar data; plotting an angular power spectrum on the basis of the compensated radar data, determining a target peak point on the basis of a peak point in the angular power spectrum, and, on the basis of the target peak point and the geometric center of the dynamic target, determining a representative point of the dynamic target, as well as the time complexity of the representative point; and on the basis of the time complexity corresponding to the representative point and a target velocity estimate V cfar , determining a true velocity V true of the dynamic target. By applying the method provided in the present application, the accuracy of velocity estimation and computational performance are improved.

Description

A hypothetical phase-compensation method for resolving velocity ambiguity Technical Field

[0001] This application relates to the field of electrical data processing technology, and more specifically, to a hypothetical phase compensation method for resolving velocity fuzziness. Background Technology

[0002] In radar systems, Doppler shift is a means of measuring the velocity of a target relative to the radar. However, velocity ambiguity occurs when the Doppler shift caused by the target's velocity exceeds the maximum frequency that the radar system can process. While MIMO radar can provide richer target information, such as additional information like target height and contour, its operating principle significantly reduces the unambiguous detection velocity range of the radar system. Incorrect target velocity estimation can affect the accuracy of target angle estimation; therefore, resolving velocity ambiguity is crucial for MIMO imaging radar. Methods based on pulse repetition frequency (PRF) variations are common solutions to the velocity ambiguity problem, including the Chinese Remainder Theorem (CRT) method, lookup table methods, and one-dimensional set algorithms.

[0003] The Chinese Remainder Theorem (CRT) is an effective method for eliminating phase uncertainty in Doppler frequency shift detection. It obtains the unambiguous result by establishing a system 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 for resolving Doppler ambiguity is quite accurate and fast, its accuracy drops rapidly when the remainders are biased by 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 for solving velocity ambiguity in pulse Doppler (PD) radar. This method overcomes the velocity ambiguity problem by utilizing multi-carrier frequency waveforms and the Chinese Remainder Theorem, and on this basis, derived an improved algorithm with better robustness. Xiao et al. proposed a robust phase expansion method based on the Chinese Remainder Theorem (CRT) and applied it in moving target synthetic aperture radar (SAR) imaging. This algorithm only performs a one-dimensional search, thus greatly reducing complexity. Both algorithms seek overlap by expanding the signal within different aliasing periods or by using algebraic techniques to expand different PRF estimates within different aliasing periods. This concept of searching for overlap estimates allows the application of clustering to resolve velocity ambiguity, and an improved clustering algorithm is proposed. This algorithm is compared with an improved Chinese Remainder Theorem (CRT) algorithm, and the results show that the improved algorithm has a lower false alarm probability and can be extended to multi-target and clutter scenarios.

[0004] The classic lookup table method quickly matches the observed Doppler frequency shift to find the true velocity of the target by pre-calculating and storing the velocity-Doppler frequency shift relationship under different conditions. Based on this idea, a simple algorithm for range and velocity ambiguity based on residual algorithms is proposed. This algorithm uses a residual lookup table to obtain unambiguous results and uses another algorithm to solve the special problem of consistent filter bandwidth across frequencies. While this method is simple and easy to implement in engineering, the table storage requires a large amount of storage space, and the lookup table is divided into degrees according to distance units, wasting a lot of time on unnecessary numerical calculations, making it difficult to meet real-time processing requirements and adapt to complex scenarios.

[0005] To address the technical challenges of velocity ambiguity in existing MIMO radar technologies, such as difficulty in achieving real-time processing, processing complexity, and large storage requirements, it is necessary to introduce an improved hypothetical phase compensation method. This method simplifies the velocity ambiguity resolution process and reduces its complexity in road-tested multi-channel MIMO millimeter-wave radar by considering the factors influencing angle estimation due to velocity ambiguity. It also reduces the number of target points the algorithm needs to process, ensures the accuracy of velocity estimation, accelerates the process of obtaining target velocity and angle in MIMO radar imaging processing, reduces abnormal velocity points, and thus improves the accuracy of velocity estimation and computational performance. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a hypothetical phase compensation method for resolving velocity ambiguity. Based on the factors influencing angle estimation due to velocity ambiguity, this invention reduces the number of target points for dynamic target detection and provides an improved hypothetical phase compensation algorithm formula for resolving velocity ambiguity. This solves the technical problems of velocity ambiguity in MIMO radar, difficulty in achieving real-time processing, complex processing, and large data storage space in existing technologies. It simplifies the processing flow and complexity of velocity ambiguity resolution in road-tested multi-channel MIMO millimeter-wave radar using hypothetical phase compensation, reduces the number of target points the algorithm needs to process, and ensures the accuracy of velocity estimation. This accelerates the processing flow for obtaining target velocity and angle in MIMO radar imaging, reduces abnormal velocity points, and thus improves the real-time performance, accuracy, and computational performance of velocity estimation.

[0007] This invention provides a hypothetical phase-compensated method for resolving velocity ambiguity, the method comprising:

[0008] S1, Define the phase compensation data structure based on the assumed influence parameters of velocity ambiguity resolution; S2, Based on the MIMO millimeter-wave radar, constant false alarm rate detector, and the phase compensation data structure, identify and detect dynamic targets, acquire radar data, and the corresponding Doppler phase difference of the dynamic targets. S3, perform Doppler phase compensation on the radar data according to the assumed velocity ambiguity period N and the phase compensation formula to obtain the compensated radar data; S4, plot the angular power spectrum based on the compensated radar data, determine the target peak point based on the peak point in the angular power spectrum, and determine the representative point of the dynamic target and the time complexity of the representative point based on the target peak point and the geometric center of the dynamic target; S5, based on the time complexity corresponding to the representative point and the target velocity estimate V... cfar Determine the true velocity V of the dynamic target. true And the complexity reduction factor M.

[0009] Preferably, in step S1: the phase compensation data structure includes: the number of transmitting antennas M TX Number of receiving antennas M RX The 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, and true velocity V. true Phase compensation value Phase compensation value m, target velocity estimate V cfar Doppler phase difference Maximum speed V max .

[0010] Preferably, step S2 includes: S21, obtaining the channel dataset S of each target point detected by the MIMO millimeter-wave radar and the constant false alarm rate detector, based on the number of transmitting antennas and receiving antennas of the MIMO millimeter-wave radar and the target point of the dynamic target. i 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 actual phase compensation value Determine the Doppler phase difference of each target point The detection result is X. (w，z) 0 <w<M TX 0 <z<M RX M TX M represents the number of transmitting antennas of the MIMO millimeter-wave radar. RX The number of receiving antennas of the MIMO millimeter-wave radar; the Doppler phase difference of the target point. The true phase compensation value for each of the target points N is the assumed velocity ambiguity period, N∈Z.

[0011] Preferably, step S2 further includes: acquiring a channel dataset S for each target point based on the MIMO millimeter-wave radar detecting the channels of each target point. i And based on the channel dataset S of each of the target points i Doppler phase difference Determine the phase value that needs compensation Among them, S i The radar data representing the target point acquired in the i-th channel, where i is the channel number corresponding to each target point, and i is an integer greater than 1; the phase value that the target point needs to compensate for.

[0012] Preferably, step S2 further includes: the entire channel dataset. Assuming the velocity ambiguity period N is determined based on 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 odd, When the number of transmitting antennas of the MIMO millimeter-wave radar is even, The radar data is the raw, all-channel data acquired from the channels corresponding to each target point detected by the MIMO millimeter-wave radar of the dynamic target, including the target point, the number of target points n, the representative point, the number of representative points a, and the true velocity V. true Phase compensation value Phase compensation value m, target velocity estimate V cfar Doppler phase difference and maximum speed V max .

[0013] Preferably, step S3 includes: detecting the channel dataset S of each target point based on the MIMO millimeter-wave radar. i The radar data is subjected to Doppler compensation calculations based on the assumed velocity ambiguity period N and a phase compensation formula to obtain the compensated radar data corresponding to each target point; wherein, the compensation formula is... S' i The compensated radar data corresponding to each of the aforementioned target points. Let i be the phase value that needs to be compensated for at the target point, and let i be the channel number corresponding to each target point. Both i and j are greater than 1 and less than or equal to M. TX integers, M TX This represents the number of transmitting antennas of the MIMO millimeter-wave radar.

[0014] Preferably, in step S4, the step of plotting the angular power spectrum based on the compensated radar data, determining the target peak point based on the peak point in the angular power spectrum, and determining the representative point of the dynamic target based on 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 true phase compensation value corresponding to each target point of the dynamic target. S42. Plot the angular power spectrum; determine the target peak point and target peak value corresponding to each target point from the peak points in the curve of the angular power spectrum, sort the target peak points to determine the maximum peak point and maximum peak value, and the time complexity of the maximum peak point, wherein the time complexity of the maximum peak point is O(n(m+mlogm)), where n is the number of target points and m is the number of phase compensation values; S43. Determine the representative point of the dynamic target based on the target peak points and the geometric center of the dynamic target, and determine the time complexity of the representative point based on the time complexity of the maximum peak point.

[0015] Preferably, step S43 further includes: connecting the target peak point and the geometric center of the dynamic target, and determining the center point of the connection and the time complexity; determining the representative point based on 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 based on target peak points near the center point; determining the time complexity of the representative point based on the number of representative points and the time complexity of the maximum peak point; wherein the time complexity of the representative point is O(a(m+mlogm)), where a is the number of representative points and m is the number of phase compensation values.

[0016] Preferably, step S5 further includes: S51, improving and optimizing the time complexity corresponding to the dynamic target based on the time complexity of the representative point and the center point of the line connecting the target peak point and the geometric center of the dynamic target, to obtain the hypothetical phase compensation solution velocity fuzzy algorithm formula and the complexity reduction factor M, wherein the hypothetical phase compensation solution velocity fuzzy algorithm formula is O(n) + O(m + mlogm), and the complexity reduction factor M... n is the number of target points, m is the number of phase compensation values; S52, based on the assumed phase compensation solution velocity fuzzy algorithm formula and the target velocity estimate V cfar Determine the true velocity V of the dynamic target true Wherein, the true velocity V of the dynamic target true =V cfar +NV max Vcfar V is the estimated target velocity. max Let N be the maximum speed of the dynamic target, and N be the assumed speed fuzzy period.

[0017] This invention, by applying the above technical solutions, addresses the influencing factors of angle estimation based on velocity ambiguity. It reduces the number of target points for dynamic target detection and provides an improved hypothetical phase compensation algorithm for resolving velocity ambiguity. This solves the technical problems of velocity ambiguity in existing MIMO radar technologies, including difficulty in achieving real-time processing, complex processing, and large data storage space. It simplifies the velocity ambiguity resolution process and reduces the complexity of hypothetical phase compensation in road-tested multi-channel MIMO millimeter-wave radar, reducing the number of target points the algorithm needs to process while ensuring the accuracy of velocity estimation. It accelerates the process of obtaining target velocity and angle in MIMO radar imaging processing, reduces abnormal velocity points, and thus improves the real-time performance, accuracy, and computational performance of velocity estimation. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 shows a schematic flowchart of a hypothetical phase compensation method for resolving velocity ambiguity proposed in an embodiment of the present invention;

[0020] Figure 2 shows a schematic diagram of three phase compensation assumptions;

[0021] Figure 3 shows a schematic diagram of the power amplitude curves of the three hypothetical signals in the angular power spectrum;

[0022] Figure 4 shows a schematic diagram of the process for selecting the peak point as the target representative point;

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

[0024] Figure 6 shows a schematic diagram of the representative point selection method that combines the peak point with the RDM geometric center point;

[0025] Figure 7 shows a comparison of the time complexity of the HPC algorithm before and after the improvement under different target point numbers;

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

[0027] Figure 9 shows a two-dimensional schematic diagram of the defuzzification results of the improved HPC algorithm for moving targets. Detailed Implementation

[0028] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] This invention provides a hypothetical phase-compensated velocity ambiguity resolution method, as shown in Figures 1 to 9. The method includes the following steps:

[0030] S1. Define the phase compensation data structure based on the assumed influence parameters of the phase compensation solution velocity ambiguity.

[0031] In this embodiment, in step S1: the phase compensation data structure includes: the number of transmitting antennas M TX Number of receiving antennas M RX 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, and actual velocity V. true Phase compensation value Phase compensation value m, target velocity estimate V cfar Doppler phase difference Maximum speed V max .

[0034] S2, based on MIMO millimeter-wave radar, constant false alarm rate detector, and the phase compensation data structure, identifies and detects dynamic targets, acquires radar data, and obtains the Doppler phase difference corresponding to the dynamic target. The complete channel dataset S and the assumed velocity ambiguity period N.

[0035] In this embodiment, step S2 includes:

[0036] S21, based on the number of transmitting and receiving antennas of the MIMO millimeter-wave radar and the target points of the dynamic target, obtain the channel dataset S of each target point detected by the MIMO millimeter-wave radar and the constant false alarm rate detector. i True phase compensation value And the sequence of detection results, and the assumed velocity ambiguity period N;

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

[0038] in,

[0039] The detection result is X. (w，z) 0 <w<M TX 0 <z<M RX M TX M represents the number of transmitting antennas of the MIMO millimeter-wave radar. RX The number of receiving antennas of the MIMO millimeter-wave radar;

[0040] The Doppler phase difference at the target point The true phase compensation value for each of the target points N is the assumed velocity ambiguity period, N∈Z.

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

[0042] Based on the MIMO millimeter-wave radar, the channels of each target point are detected, and the channel dataset S of each target point is obtained. i And based on the channel dataset S of each of the target points i Doppler phase difference Determine the phase value that needs compensation

[0043] in,

[0044] S i The radar data of the target point obtained in the i-th channel represents the target point, where i is the channel number corresponding to each target point and i is an integer greater than 1.

[0045] The phase value that the target point needs to compensate for

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

[0047] All channel datasets

[0048] Assuming the velocity ambiguity period N is determined based on 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 odd, When the number of transmitting antennas of the MIMO millimeter-wave radar is even,

[0049] The radar data is the raw, all-channel data acquired from the channels corresponding to each target point detected by the MIMO millimeter-wave radar of the dynamic target, including the target point, the number of target points n, the representative point, the number of representative points a, and the true velocity V. true Phase compensation value Phase compensation value m, target velocity estimate V cfar Doppler phase difference and maximum speed V max .

[0050] S3. Based on the assumed velocity ambiguity period N and the phase compensation formula, Doppler phase compensation is performed on the radar data to obtain the compensated radar data.

[0051] In this embodiment, step S3 includes:

[0052] Based on the channel dataset S of the MIMO millimeter-wave radar detecting each target point i And, assuming the velocity ambiguity period N and the phase compensation formula, perform Doppler compensation calculation on the radar data to obtain the compensated radar data corresponding to each target point;

[0053] in,

[0054] The compensation formula is as follows: S' i The compensated radar data corresponding to each of the aforementioned target points. Let i be the phase value that needs to be compensated for at the target point, and let i be the channel number corresponding to each target point. Both i and j are greater than 1 and less than or equal to M. TX integers, M TX This represents the number of transmitting antennas of the MIMO millimeter-wave radar.

[0055] 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 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, step S4, which involves plotting the angular power spectrum based on the compensated radar data, determining the target peak point based on the peak points in the angular power spectrum, and determining the representative point of the dynamic target based on 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 true phase compensation value corresponding to each target point of the dynamic target. Plot the angular power spectrum;

[0058] S42, determine the target peak point and target peak value corresponding to each target point from the peak points in the curve of the angular power spectrum, sort the target peak points to determine the maximum peak point and maximum peak value, and the time complexity of the maximum peak point, wherein the time complexity of the maximum peak point is O(n(m+mlogm)), where n is the number of target points and m is the number of phase compensation values;

[0059] S43, determine the representative point of the dynamic target based on the target peak point and the geometric center of the dynamic target, and determine the time complexity of the representative point based on the time complexity of the maximum peak point.

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

[0061] Connect the target peak point and the geometric center of the dynamic target with a line, and determine the center point of the line and the time complexity. Determine the representative point based on the distance between the target peak point and the center point. The representative point is a target peak point whose distance from the center point is less than a preset value. The preset value is set based on the target peak points near the center point.

[0062] The time complexity of the representative point is determined based on the number of representative points and the time complexity of the maximum peak point.

[0063] The time complexity of the representative point is O(a(m+mlogm)), where a is the number of representative points and m is the number of phase compensation values.

[0064] S5, based on the time complexity and target velocity estimate V corresponding to the representative point. cfar Determine the true velocity V of the dynamic target. true And the complexity reduction factor M.

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

[0066] S51, based on the time complexity of the representative point and the center point of the line connecting the target peak point and the geometric center of the dynamic target, the time complexity of the dynamic target is improved and optimized to obtain the hypothetical phase compensation solution velocity fuzzy algorithm formula and the complexity reduction factor M, wherein the hypothetical phase compensation solution velocity fuzzy algorithm formula is O(n) + O(m + mlogm), and the complexity reduction factor M... n represents the number of target points, and m represents the number of phase compensation values;

[0067] S52, based on the assumed phase compensation solution velocity fuzzy algorithm formula and the target velocity estimate V cfar Determine the true velocity V of the dynamic target true ,in,

[0068] The true velocity V of the dynamic target true =V cfar +NV max V cfar V is the estimated target velocity. max Let N be the maximum speed of the dynamic target, and N be the assumed speed fuzzy period.

[0069] By applying the above technical solutions, the influence factors of velocity ambiguity on angle estimation are addressed. This is achieved by providing and applying a reduced number of target points for dynamic target detection, along with an improved hypothetical phase compensation algorithm for resolving velocity ambiguity. This solves the technical problems of velocity ambiguity in existing MIMO radar technologies, including difficulty in achieving real-time processing, complex processing, and large data storage requirements. It simplifies the velocity ambiguity resolution process and reduces the complexity of hypothetical phase compensation in road-tested multi-channel MIMO millimeter-wave radar, reducing the number of target points the algorithm needs to process while ensuring the accuracy of velocity estimation. This accelerates the process of obtaining target velocity and angle in MIMO radar imaging processing, reduces abnormal velocity points, and thus improves the real-time performance, accuracy, and computational performance of velocity estimation.

[0070] To help those skilled in the art better understand the hypothetical phase compensation method for resolving velocity ambiguity provided by this invention, examples are provided to further illustrate the above steps.

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

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

[0073] According to the principle of radar angle measurement, angle estimation... In other words, radar angle determination mainly depends on phase difference, and the phase difference of a moving target is related to the Doppler frequency shift f. d Related. There is a coupling relationship between the angle estimation and velocity of a moving target. The ambiguity of the target velocity will cause the angle estimation to be incorrect, thus causing the target position in the final point cloud image to be offset.

[0074] Calibrating the phase change caused by target velocity using velocity ambiguity resolution techniques is crucial for the accuracy of radar imaging. When the radar range-Doppler image (RDM) containing target velocity and range information is processed by CFAR detection, an estimate of the target velocity and the Doppler phase difference can be obtained. However, in practical applications, the velocity of moving targets often exceeds the maximum detectable speed of the radar, requiring a 2-transmit, 4-receiver (N) configuration. TX =2,N RX Taking a MIMO radar with a resolution of 4 as an example, a virtual antenna array with 1 transmitter and 8 receivers can be formed. Assume the result sequence after passing through the CFAR target point is X. (m,n) (0<m≤N TX 0 < n ≤ N RX If the phase compensation satisfies the formula: X′(m,n)=X(m,n)*e -jmΔφ ,(0<m≤N TX ,0<m≤N RX ).

[0075] Only by knowing the target's true speed V true Only then can the true phase compensation value be obtained. Assume the target velocity estimate after CFAR processing is V cfar The Doppler phase difference is Target true speed V true Compared with the target velocity estimate V est The Doppler phase difference that actually needs to be compensated The relationship between them is:

[0076] Where N is the velocity ambiguity period. Only when the target velocity is less than the maximum unambiguous velocity... Otherwise, the phase difference is an integer multiple of 2π.

[0077] According to the above formula, there is a multiple relationship between the target's true speed and the speed error, i.e., a speed ambiguity period. The greater the target speed, the greater the difference between the target speed detected by the constant false alarm rate (CFAR) detector and the actual target speed, leading to increased radar angle estimation errors. Furthermore, the difference between the target speed detected by the CFAR and the actual target speed continuously increases. In urban traffic environments, moving targets are mostly high-speed vehicles; therefore, the accuracy of target angle measurement depends on effectively resolving speed ambiguity.

[0078] Step 2: Improved Hypothetical Phase Compensation Method for Resolving Velocity Ambiguity

[0079] Step one analyzed the impact of velocity ambiguity on angle estimation. It is evident that when a MIMO radar imaging system performs velocity ambiguity de-ambiguity processing on a moving target, the target generally exhibits a large velocity ambiguity period. To address the impact of velocity ambiguity on angle estimation, this application introduces a hypothetical phase compensation (HPC) velocity ambiguity de-ambiguity algorithm. This algorithm can estimate the target's true velocity under conditions of large velocity ambiguity periods and perform velocity ambiguity de-ambiguity processing within a single frame. The principle of the HPC algorithm will be introduced below.

[0080] In a MIMO radar system with 4Tx, the phase difference between chirp1 and chirp5 represents the detected target Doppler frequency shift. when At this time, velocity ambiguity manifests as cyclic Doppler phase aliasing. As shown in Figure 2, the leftmost column illustrates such an aliasing case, where three different velocities have the same detection phase shift. The red trajectories within the true phase circles of each velocity region (R1, R2, and R3) are shown. R1 represents a target with a velocity less than the radar's maximum unambiguous velocity, R2 represents a target moving away from the radar at a velocity greater than the radar's maximum unambiguous velocity, and R3 represents a target approaching the radar at a velocity greater than the radar's maximum unambiguous velocity. The true phase shifts for R2 and R3 are... Therefore, HPC can be used to establish three assumptions to extend the explicit maximum unambiguous speed to ±3V. max First, based on the detected phase shift output by CFAR detection... Enumerate all phase values ​​that may require compensation:

[0081] Secondly, according to the formula The radar data is compensated sequentially using each assumed phase compensation value. The compensated data is represented as follows: Among them, S i This represents the signal data in the i-th channel, where i starts from 2, because only non-initial codes require Doppler phase compensation. This represents the phase value that needs to be compensated. Note that the number of assumptions cannot increase indefinitely; it is limited by the number of transmitting antennas N in the system. TX Due to limitations, ideally, the maximum detection speed of the HPC algorithm is N. TX ·V MAX In case R1, the target velocity is within the maximum unambiguous velocity range detectable by the radar, and the compensated phase coherently converges to the same position in H1. However, in H2 and H3, the additional phase compensation (±2π compensation) causes the phase to converge to a position other than that in H1. Compensation, when the speeds are respectively [V max 3V max ] and [-3V max,-V max When H2 and H3 are phase-focused, the angular power spectrum under each assumption is obtained after FFT in the angular dimension. The assumption of the peak point is correct, and the corresponding velocity and the angle obtained are the true velocity and angle of the target.

[0082] Figure 3 shows the signal amplitudes of the next target point in the angular power spectrum under three assumptions. H1, H2, and H3 show peaks around -10°, with H3 having the highest peak, indicating a true velocity of V. cfar -2V max .

[0083] Step 3: Selection of representative points based on geometric center and peak location

[0084] The process of using the HPC velocity de-ambiguity algorithm to solve the velocity ambiguity problem and obtain the true velocity and angle of the target is as follows:

[0085] Step 1): Before implementing the algorithm, it is necessary to determine the detection phase. The channel dataset S and the assumed velocity ambiguity period N are used. Performing the first CFAR detection on the target data acquired by the radar yields the target's range and Doppler information in the radar range-Doppler image (RDM), and the detection phase is calculated. Simultaneously, data is extracted from the same index position in the radar range-Doppler map (RDM) of each channel to form a channel dataset: The value of N depends on the number of transmitting antennas M. TX Let's calculate. Where N satisfies: when the number of transmitting antennas is odd, |N| <= (M) TX -1) / 2, when the number of transmitting antennas is even, |N|<=(M TX / 2)-1.

[0086] Step 1): Based on the N value, list all HPC cases, and compensate the radar data sequentially using each assumed phase compensation value, where H satisfies the equation. Then, an FFT operation was performed on each compensated data point (2N+1 in total) to obtain the angular power spectrum under each assumption. Where H = H -N ,…,H k ,…,H N .

[0087] Step 3): Select the assumption of the peak point in the angular power spectrum, and the corresponding velocity and the obtained angle are the true values ​​of the target.

[0088] The HPC velocity ambiguity resolution algorithm requires the aforementioned processing for each target point detected by CFAR. The time complexity of listing all HPC scenarios for a target point depends on the number of assumed phase compensation values, denoted as m. After performing FFT operations on all obtained compensation values ​​to obtain the angular power spectrum, the peak values ​​after all assumed phase compensation need to be quickly sorted. The time complexity of selecting the maximum peak point is O(mlogm). Therefore, the overall time complexity of this step is O(m+mlogm). Assuming a moving target is detected as n target points by a MIMO radar, CFAR detection can generally be considered to have a linear time complexity, i.e., O(n), where n is the number of target points. Thus, the time complexity of the entire HPC velocity ambiguity resolution process is O(n·(m+mlogm)), where the value of m is related to the number of transmit antennas. Therefore, the time complexity of the HPC velocity ambiguity resolution algorithm is mainly affected by the number of target points n.

[0089] MIMO radar, due to its high spatial resolution, can decompose a single target into multiple target points. However, this high resolution leads to an increased number of target points. In the process of processing each target point to eliminate velocity ambiguity, applying the HPC velocity ambiguity resolution algorithm to each target point significantly increases the algorithm's time complexity, thereby reducing the system's processing speed. Furthermore, the high energy of sidelobe points may cause velocity expansion, resulting in noise in the point cloud data. Since these target points actually represent the same target, if a (a << n) representative points are selected for HPC velocity ambiguity resolution, their velocity values ​​represent the overall velocity value, and the phase compensation value 2Nπ represents the phase compensation value for all points. Therefore, the time complexity of the HPC velocity ambiguity resolution process is O(a·(m+mlogm)).

[0090] This method significantly reduces the time complexity of the HPC defuzzification process and improves the system's processing efficiency.

[0091] There are two ways to select a representative point. First, the point with the strongest energy in the target point cloud image, i.e., the peak point, can be selected as the representative point. However, this method has limitations. While the peak point, as the highest energy point of the target, does represent the location of the strongest reflection, this location is not always consistent with the geometric center of the target. As shown in Figure 4, in a traffic scenario, radar is generally installed on both sides of the road. When the peak point is selected as the representative point, the relative azimuth angle between the target and the radar is obtained as θ', while the actual angle is θ, resulting in an angle error: Δθ = θ - θ'. Therefore, relying solely on the point with the highest reflection energy to represent the entire target may lead to errors in angle estimation. This error may ultimately manifest as a target position shift in the imaging results, affecting imaging quality and the accuracy of target localization.

[0092] On the other hand, selecting a representative point based on the geometric center of the target in the radar range-Doppler image (RDM) is another method. As shown in Figure 5, the advantage of using the geometric center as a representative point is that it can provide a point that more accurately represents the overall position of the target. When the radar position is close to the sides of the road to observe vehicle targets, the obtained target points may present an L-shaped arrangement. In this layout, the white dots represent the geometric center points obtained from the target radar range-Doppler image (RDM) analysis. However, the geometric center point is not always located in the area of ​​highest target energy. Because MIMO radar has high range and velocity resolution, the target energy can diffuse, causing the point to potentially be located within the radar sidelobe region. 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 the imaging results.

[0093] Choosing the peak point as a representative point can reflect the target's highest energy level, but it may not reflect the target's accurate location. While choosing the target's radar range-Doppler (RDM) geometric center is closer to the target's true geometric center, this point may introduce errors due to its location within the radar sidelobe region. Therefore, this application proposes a representative point selection strategy to combine the advantages of both methods. As shown in Figure 6, the red dot represents the target's actual geometric center. Since different moving targets have different geometric centers—for example, the geometric center of a car is located inside the car and cannot be directly detected by radar—this application adopts the strategy of finding an optimal representative point located near the midpoint of the line connecting the target's peak point and the target's radar range-Doppler (RDM) geometric center. This point is then used as the representative point.

[0094] Step 4: Algorithm Performance Simulation and Comparative Analysis

[0095] This section verifies the effectiveness of the proposed improved hypothetical phase-compensated velocity fuzzing algorithm, specifically from two dimensions: time complexity and accuracy of the target velocity fuzzing solution.

[0096] First, we verify the time complexity. We assume the time complexity of the phase compensation velocity ambiguity resolution algorithm is O(n·(m+mlogm)), where n is the number of target points and m is the number of assumed phase compensation values, which is 5 for a 12-transmit, 16-receive MIMO radar. Next, we calculate the time complexity of the improved assumed phase compensation velocity ambiguity resolution algorithm. For a target with n target points, first, finding the peak point and the radar range-Doppler map (RDM) geometric center point takes O(n) time. Then, calculating the center point between the two points takes O(1) time. Finally, finding the point closest to the target point, i.e., the representative point, takes O(n) time. Combining the above steps, the total computational complexity is mainly determined by the operation of traversing n points, so the total complexity is O(n). The time complexity of HPC processing on the representative point is O((m+mlogm)). The improved hypothetical phase compensation velocity fuzzy resolution algorithm has a time complexity of O(n) + O(m + mlogm). The relationship between the time complexity of the hypothetical phase compensation velocity fuzzy resolution algorithm before and after the improvement is: M = O(n) + O(m + mlogm) / O(n·(m + mlogm)), where M is the factor by which the algorithm complexity is reduced.

[0097] Since small cars are the most common moving targets in traffic scenarios, this application uses a small car as an example to verify the algorithm. Assuming the car length is approximately 4.5m, commonly used traffic radars, due to their low range and speed resolution, typically identify a small car as about 10 target points. However, in MIMO radar imaging systems, due to the improved range, speed, and angular resolution, as well as the shorter imaging distance, the size of the target can be presented as accurately as possible; a small car can be identified as approximately 400 target points. Figure 7 shows the relationship between the number of target points and the algorithm's time complexity, with the time complexity decreasing by a factor satisfying the formula: M = O(n) + O(m + mlogm) / O(n·(m + mlogm)). As the number of target points increases, the time complexity of the HPC algorithm increases. Compared to the original HPC algorithm, whose time complexity is highly sensitive to the number of target points, the improved HPC algorithm has significantly lower time complexity and exhibits a more gradual increase in the number of target points, demonstrating stronger robustness. For MIMO radar imaging systems, achieving spatial stereo imaging of moving targets requires high spatial resolution, which means a single target is resolved into multiple points, increasing processing complexity. When processing high-resolution MIMO radar data, the time complexity of the original HPC algorithm increases rapidly with the number of target points, leading to inefficiency and excessively long processing times in practical applications, especially with a large number of target points. In contrast, the improved HPC algorithm maintains both performance and efficiency more effectively when processing high-resolution MIMO radar data, thus meeting the requirements for spatial stereo imaging.

[0098] Taking the dynamic target shown in Figures 8 and 9 as an example, assuming the radar is located to the left rear of the target and the actual speed of the car is approximately 25 km / h, the accuracy of the improved HPC algorithm in resolving target velocity ambiguity is first verified using a two-dimensional point cloud image of the target. Figure 8 shows the vehicle point cloud image obtained using the original HPC velocity ambiguity resolution algorithm. It can be seen from the point cloud image that the velocity values ​​after resolving ambiguity using the original HPC algorithm have errors, resulting in a more dispersed point cloud and a significant deviation between the point cloud image and the target's true position. This is because the original HPC algorithm requires calculating all possible hypothetical phase compensation values ​​for all target points when resolving velocity ambiguity. However, in MIMO radar systems, due to sidelobe effects and limitations in velocity resolution, the target energy expands in the range and Doppler dimensions, leading to errors in the obtained velocity after ambiguity resolution. These errors, in turn, affect the accuracy of phase compensation, ultimately causing the target position to deviate from its true position. Figure 9 shows the vehicle point cloud image obtained using the improved HPC velocity ambiguity resolution algorithm. By selecting representative point velocities to define the target velocity and performing phase compensation on the target, this processing method ensures that the obtained target velocity is basically consistent with the true velocity, and the position is also close to the true position. As shown in Table 1, the improved HPC speed defuzzification algorithm has a shorter running time and the defuzzified speed value is closer to the car's actual speed.

[0099] Table 1 Comparison of velocity fuzzing resolution results of HPC algorithm before and after improvement for moving targets.

[0100] By applying the above technical solutions, this application studies the velocity ambiguity problem in MIMO millimeter-wave radar systems and the hypothetical phase compensation (HPC) algorithm for resolving velocity ambiguity. First, the causes of velocity ambiguity and its impact on target angle estimation are discussed. Then, the basic principles and implementation flow of the hypothetical phase compensation algorithm for resolving velocity ambiguity are explained, and theoretical analysis demonstrates the high time complexity of the original HPC algorithm in MIMO radar systems. Finally, an improved hypothetical phase compensation algorithm for resolving velocity ambiguity is proposed. Based on the target characteristics acquired by MIMO radar, this algorithm proposes a method that combines the geometric center point and peak points to select representative target points. This method significantly reduces the number of target points that the algorithm needs to process, while ensuring the accuracy of velocity estimation, accelerating the processing flow of obtaining target velocity and angle in MIMO radar imaging, and reducing abnormal velocity points. Simulation results show that compared to the original HPC algorithm, the improved HPC algorithm for resolving velocity ambiguity has lower time complexity and accurate velocity estimation capability.

[0101] The various embodiments in this specification are described in a related manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0102] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A hypothetical phase-compensated method for resolving velocity ambiguity, characterized in that, The method includes: S1. Define the phase compensation data structure based on the assumed influence parameters of the velocity ambiguity in the phase compensation solution; S2, based on MIMO millimeter-wave radar, constant false alarm rate detector, and the phase compensation data structure, identifies and detects dynamic targets, acquires radar data, and obtains the Doppler phase difference corresponding to the dynamic target. The complete channel dataset S and the assumed velocity ambiguity period N; S3, Doppler phase compensation is performed on the radar data according to the assumed velocity ambiguity period N and the phase compensation formula to obtain the 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 and target velocity estimate V corresponding to the representative point. cfar Determine the true velocity V of the dynamic target. true And the complexity reduction factor M; Step S2 includes: S21, based on the number of transmitting and receiving antennas of the MIMO millimeter-wave radar and the target points of the dynamic target, obtain the channel dataset S of each target point detected by the MIMO millimeter-wave radar and the constant false alarm rate detector. i True phase compensation value And the sequence of detection results, and the assumed velocity ambiguity period N; S22, based on the assumed velocity ambiguity period N and the actual phase compensation value Determine the Doppler phase difference of each target point in, The detection result is X. (w，z) 0 <w<M TX 0 <z<M RX M TX M represents the number of transmitting antennas of the MIMO millimeter-wave radar. RX The number of receiving antennas of the MIMO millimeter-wave radar; The Doppler phase difference at the target point The true phase compensation value for each of the target points N is the assumed velocity ambiguity period, N∈Z; Step S3 includes: Based on the channel dataset S of the MIMO millimeter-wave radar detecting each target point i And, assuming the velocity ambiguity period N and the phase compensation formula, perform Doppler compensation calculation on the radar data to obtain the compensated radar data corresponding to each target point; in, The compensation formula is as follows: S' i The compensated radar data corresponding to each of the aforementioned target points. Let i be the phase value that needs to be compensated for at the target point, and let i be the channel number corresponding to each target point. Both i and j are greater than 1 and less than or equal to M. TX integers, M TX This represents the number of transmitting antennas of the MIMO millimeter-wave radar.

2. The method as described in claim 1, characterized in that, In step S1, the phase compensation data structure includes: the number of transmitting antennas M TX Number of receiving antennas M RX 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, and actual velocity V. true Phase compensation value Phase compensation value m, target velocity estimate V cfar Doppler phase difference Maximum speed V max .

3. The method as described in claim 1, characterized in that, Step S2 further includes: Based on the MIMO millimeter-wave radar, the channels of each target point are detected, and the channel dataset S of each target point is obtained. i And based on the channel dataset S of each of the target points i Doppler phase difference Determine the phase value that needs compensation in, S i The radar data of the target point obtained in the i-th channel represents the target point, where i is the channel number corresponding to each target point and i is an integer greater than 1. The phase value that the target point needs to compensate for 4. The method as described in claim 1, characterized in that, Step S2 further includes: All channel datasets Assuming the velocity ambiguity period N is determined based on 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 odd, When the number of transmitting antennas of the MIMO millimeter-wave radar is even, The radar data is the raw, all-channel data acquired from the channels corresponding to each target point detected by the MIMO millimeter-wave radar of the dynamic target, including the target point, the number of target points n, the representative point, the number of representative points a, and the true velocity V. true Phase compensation value Phase compensation value m, target velocity estimate V cfar Doppler phase difference and maximum speed V max .

5. The method as described in claim 1, characterized in that, In step S4, the step of plotting the angular power spectrum based on the compensated radar data, determining the target peak point based on the peak points in the angular power spectrum, and determining the representative point of the dynamic target based on 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 true phase compensation value corresponding to each target point of the dynamic target. Plot the angular power spectrum; S42, determine the target peak point and target peak value corresponding to each target point from the peak points in the curve of the angular power spectrum, sort the target peak points to determine the maximum peak point and maximum peak value, and the time complexity of the maximum peak point, wherein the time complexity of the maximum peak point is O(n(m+m log m)), where n is the number of target points and m is the number of phase compensation values; S43, determine the representative point of the dynamic target based on the target peak point and the geometric center of the dynamic target, and determine the time complexity of the representative point based on the time complexity of the maximum peak point.

6. The method as described in claim 5, characterized in that, Step S43 further includes: Connect the target peak point and the geometric center of the dynamic target with a line, and determine the center point of the line and the time complexity. Determine the representative point based on the distance between the target peak point and the center point. The representative point is a target peak point whose distance from the center point is less than a preset value. The preset value is set based on the target peak points near the center point. The time complexity of the representative point is determined based on the number of representative points and the time complexity of the maximum peak point. The time complexity of the representative point is O(a(m+m log m)), where a is the number of representative points and m is the number of phase compensation values.

7. The method as described in claim 1, characterized in that, Step S5 further includes: S51, based on the time complexity of the representative point and the center point of the line connecting the target peak point and the geometric center of the dynamic target, the time complexity of the dynamic target is improved and optimized to obtain the hypothetical phase compensation solution velocity fuzzy algorithm formula and the complexity reduction factor M, wherein the hypothetical phase compensation solution velocity fuzzy algorithm formula is O(n) + O(m + m log m), and the complexity reduction factor M... n represents the number of target points, and m represents the number of phase compensation values; S52, based on the assumed phase compensation solution velocity fuzzy algorithm formula and the target velocity estimate V cfar Determine the true velocity V of the dynamic target true ,in, The true velocity V of the dynamic target true =V cfar +NV max V cfar V is the estimated target velocity. max Let N be the maximum speed of the dynamic target, and N be the assumed speed fuzzy period.

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