A doppler velocity filtering multipath false target suppression method, device and medium

Abstract

The application discloses a Doppler velocity filtering multipath false target suppression method, equipment and medium, and belongs to the technical field of millimeter wave radar target detection. The method comprises the following steps: acquiring radar point cloud data; separating dynamic and static point cloud data from radar original echo data to obtain a static point cloud set and a dynamic point cloud set; clustering the static point cloud set, fitting a linear boundary according to a clustering result, and obtaining a straight line equation of a reflection boundary; estimating Doppler velocities of first and second order multipath false targets corresponding to each track in the dynamic point cloud set respectively to obtain corresponding Doppler velocity search intervals; traversing the dynamic point cloud set, performing global point cloud Doppler velocity filtering on the obtained search intervals, and obtaining a filtered dynamic point cloud set; and performing multi-dimensional information judgment on the filtered dynamic point cloud set to identify multipath false targets, so as to realize multipath false target suppression. The application effectively utilizes Doppler information, thereby effectively identifying and suppressing multipath false targets.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of millimeter wave radar target detection, and in particular to a Doppler velocity filtering multipath false target suppression method, device and medium. BACKGROUND

[0002] Millimeter wave radar has a wide range of applications in target detection, positioning and identification in complex environments. The path from the radar to the target where the electromagnetic wave is directly reflected by the radar is called the direct path. However, due to the presence of various static reflecting surfaces in the environment, the electromagnetic wave is reflected multiple times by the reflecting surfaces during transmission, resulting in multipath signal components in addition to the direct path signal when detecting and identifying the real target. Since the energy of the millimeter wave radar is greatly attenuated after multiple reflections, only the first and second reflections are considered when detecting the real target. The false alarm caused by the first and second multipath false targets formed by the static reflecting surfaces in the environment greatly interferes with the identification and tracking of the real target. Therefore, it is of great significance to identify and suppress the multipath false targets caused by the static reflecting surfaces detected by the millimeter wave radar.

[0003] In recent years, many research institutions at home and abroad have carried out research on the identification and suppression of multipath false targets under vehicle-mounted millimeter wave radar and traffic radar detection. The first prior art scheme proposes a multipath suppression method based on the correlation of true and false target trajectories. This method uses the correlation between the tracked target and its mirror false target trajectory to evaluate the correlation between candidate track pairs through three criteria to classify false target tracks, thereby suppressing multipath false targets. The evaluation criteria of this method depend on the track correlation of the matching pair of real and false targets. When the amount of point cloud data is large, the algorithm has high complexity and large computational load, which is not suitable for multi-target scenarios in complex scenarios. The second prior art scheme proposes a multipath model and false target suppression method based on millimeter wave road monitoring radar. This method extracts eight features such as point track distance, azimuth angle, velocity and signal-to-noise ratio based on the detected point cloud data and classifies them based on a fully connected neural network, achieving a multipath false target recognition rate of 95.74% and an overall recognition rate of 90.70%, thereby achieving effective detection and positioning of vehicle targets. The above method needs to obtain the true value with the aid of a camera sensor, and the data set is made by manual annotation, which is time-consuming and has certain errors. The complexity of the multi-dimensional feature data set and the neural network structure is a constraint factor for the real-time performance of the road radar. Therefore, it is of great value to study the identification and suppression of multipath false targets caused by static reflecting surfaces in millimeter wave radar detection. SUMMARY

[0004] To at least partially solve one of the technical problems existing in the prior art, the present application aims to provide a Doppler velocity filtering multipath false target suppression method, device and medium based on reflection boundary fitting.

[0005] The first technical solution adopted by the present application is:

[0006] A Doppler velocity filtering multipath false target suppression method, comprising the following steps:

[0007] Obtaining radar point cloud data;

[0008] Based on Doppler velocity information, separating dynamic and static point cloud data from radar original echo data to obtain a static point cloud set and a dynamic point cloud set;

[0009] Clustering the static point cloud set, and performing linear boundary fitting according to the clustering result to obtain a straight line equation of the reflection boundary;

[0010] Based on a multipath propagation model, estimating the Doppler velocity of the corresponding first-order multipath false target and the Doppler velocity of the corresponding second-order multipath false target for each track in the dynamic point cloud set to obtain a corresponding Doppler velocity search interval;

[0011] Traversing the dynamic point cloud set, performing global point cloud Doppler velocity filtering on the obtained search interval to obtain a filtered dynamic point cloud set;

[0012] Performing multi-dimensional information decision on the filtered dynamic point cloud set to identify multipath false targets to achieve multipath false target suppression.

[0013] Further, the expression of the radar point cloud data is:

[0014]

[0015] In the formula, p g is the gth track in the point cloud set Ω; x g , y g represent the position coordinates of the gth track; r g represents the radial distance of the gth track; v represents the Doppler velocity of the gth track; v g represents the motion speed of the gth track; θ g represents the azimuth angle of the gth track; rcs g represents the scattering area of the gth track; snr g represents the signal-to-noise ratio of the gth track; power g represents the signal power of the gth track; and N is the number of tracks.

[0016] Further, the static point cloud set is clustered, linear boundary fitting is performed according to a clustering result, and a straight line equation of the reflection boundary is obtained.

[0017] The static point cloud set is clustered by using a density-based clustering method, and a Hough transform is used to fit the reflection boundary in each clustering cluster to obtain a straight line equation of the reflection boundary.

[0018] Further, the static point cloud set is clustered by using a density-based clustering method, and a Hough transform is used to fit the reflection boundary in each clustering cluster, and the Hough transform comprises the following steps.

[0019] The static point cloud of each frame is subjected to DBSCAN processing to obtain M static point cloud clusters;

[0020] All data points (x i , y i ) in the i-th cluster are used to obtain a Hesse normal line by using the following formula:

[0021] ρ = x i cos θ + y i sin θ

[0022] In the formula, ρ represents the perpendicular distance from the origin to the straight line passing through the point (x i , y i ), θ represents the rotation angle of the perpendicular line, and the range of ρ (ρ min , ρ max ) and the range of θ (θ min , θ max ) are determined according to the distribution characteristics of the static point cloud.

[0023] All curve sets C corresponding to the data points in the clustering cluster are obtained;

[0024] The intersection (θ j , ρ j ) of the curve set C corresponding to the point set of each clustering cluster is estimated;

[0025] Finally, parameter estimation is performed on the fitted straight line.

[0026] It is assumed that the fitted straight line equation of the reflection boundary in the Cartesian coordinate system is:

[0027] Ax + By + C = 0

[0028] Based on the estimated intersection coordinates (θ j , ρ j ), the Hesse normal line parameters are converted into general straight line equation parameters:

[0029]

[0030] For all data points (x i ,y i ) in the i-th cluster, based on the fitting straight line equation, traverse all static point cloud cluster, get the fitting result of each point cloud cluster, so as to realize the estimation of static reflection boundary.

[0031] Further, the estimation method of the Doppler velocity of the second-order multipath false target is as follows:

[0032] Suppose the target point track coordinates in the dynamic point cloud set D are (x t ,y t ), the parameters A, B, C obtained based on the fitting straight line equation, and calculate the second-order false target coordinates (x ts ,y ts ) of the target point track about the straight line mirror symmetry:

[0033]

[0034] Given the second-order false target point (x t ,y t ) of the target point (x ts ,y ts ) about the fitting reflection boundary, the radial distance of the theoretical second-order false target point (x ts ,y ts ) is:

[0035]

[0036] According to the differential relationship between Doppler velocity and radial distance in radar signal processing, the Doppler velocity of the theoretical second-order false target point (x ts ,y ts ) is:

[0037]

[0038] The estimation method of the Doppler velocity of the first-order multipath false target is as follows:

[0039] According to the multipath propagation geometric model, the real target point track generates first-order false targets M1, M2 about the static reflection boundary respectively; suppose the target point track coordinates in the dynamic point cloud set D are (x t ,y t ), the second-order false target point coordinates are (x ts ,y ts ), and the theoretical radial distance of the first-order false targets M1, M2 is equal according to the electromagnetic wave multipath propagation model, recorded as r M :

[0040]

[0041] where r ts is the radial distance of the second-order false target theory, r t is the radial distance of the target track;

[0042] The theoretical Doppler velocity of the first-order false target M1, M2 is obtained as follows:

[0043]

[0044] where (x t , y t ), (x ts , y ts ) are the coordinates of the target track and the coordinates of the second-order false target.

[0045] Further, the corresponding Doppler velocity search interval is obtained, comprising:

[0046] Suppose the dynamic target track is p i ∈D, D is a dynamic point cloud set, p i ={x i , y i , r i , v Di , v i , θ i , rcs i , snr i , power i}, respectively calculate the target track p i The position coordinates of the theoretical second-order false target S obtained by fitting the reflection boundary radial distance radial velocity search interval and the radial distance of the first-order false target M1, M2 radial velocity search interval Based on the search interval The Doppler velocity search interval δ i of the target track is obtained as follows:

[0047]

[0048] Further, the obtained search interval is globally filtered by the point cloud Doppler velocity to obtain a filtered dynamic point cloud set, comprising:

[0049] The Doppler velocity filtering is performed in the dynamic point cloud set D, and the track set that satisfies the search interval δ i is recorded as

[0050]

[0051] For the point trace p in the dynamic point cloud set D i , get the point trace set about this point Then all the points in the set satisfy the Doppler velocity constraint.

[0052] Furthermore, a multi-dimensional information decision maker is constructed to realize multi-dimensional information decision making, specifically:

[0053] The 3D feature vector is defined as follows:

[0054]

[0055] Among them, rcs is the scattering area of ​​the point cloud data, snr is the point cloud signal-to-noise ratio, and power is the signal power;

[0056] Define the weight vector in the 3D information decision maker for:

[0057]

[0058] Calculate the weighted sum of the output op of the decision maker as:

[0059]

[0060] The decision maker is used to calculate the output of the decision maker in the dynamic target point cloud trace and the point cloud set trace after Doppler velocity filtering, thereby realizing the identification of multipath false targets.

[0061] The second technical solution adopted by the present invention is:

[0062] An electronic device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the above-mentioned Doppler velocity filtering multipath false target suppression method.

[0063] The third technical solution adopted by the present invention is:

[0064] A computer-readable storage medium stores at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the Doppler velocity filtering multipath false target suppression method as described above.

[0065] The fourth technical solution adopted by the present invention is:

[0066] A computer program product or computer program comprises computer instructions stored in a computer-readable storage medium. The processor of the computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the above-mentioned method.

[0067] The beneficial effects of the present application are: the present application proposes a Doppler velocity filtering multi-path false target suppression scheme based on reflection boundary fitting, which can in millimeter wave radar detection, based on static radar point cloud data, adopt the boundary fitting algorithm of the least variance Hough transform, more accurately fit the static reflection surface of the multi-path signal, and based on the multi-path propagation model, filter in the Doppler velocity dimension of the dynamic point cloud, effectively utilize the Doppler information, so as to effectively identify and suppress the multi-path false target, and based on the multi-dimensional information of the point cloud data, suppress the false target without causing the loss of the real target. Compared with the multi-path suppression method mentioned in the technical background, the present application can fully utilize the point cloud data information in the Doppler velocity dimension in complex environment radar detection, and is verified in the measured data, and can effectively realize the identification and suppression of the multi-path false target. BRIEF DESCRIPTION OF DRAWINGS

[0068] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following introduces the drawings of the related technical solutions in the embodiments of the present application or the prior art. It should be understood that the drawings in the following introduction are only for the convenience of clearly describing part of the embodiments in the technical solutions of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0069] Figure 1 The flow chart of the Doppler velocity filtering multi-path false target suppression method based on reflection boundary fitting in the embodiments of the present application;

[0070] Figure 2 The radar detection scene schematic diagram in the embodiments of the present application;

[0071] Figure 3 The static road railing reflection geometric model schematic diagram in the embodiments of the present application;

[0072] Figure 4 The Doppler velocity schematic diagram under radar detection in the embodiments of the present application; wherein, Figure 4 (a) is a radar detection side view, Figure 4 (b) is a radar detection top view;

[0073] Figure 5 The measured scene diagram in the embodiments of the present application;

[0074] Figure 6 Fig. 1 is a schematic diagram of a static reflection surface multipath false area in an embodiment of the present application;

[0075] Figure 7 Fig. 2 is a schematic diagram of a measured data processing result in an embodiment of the present application; wherein, Figure 7 (a) is a single-frame point cloud in a measured scene; Figure 7 (b) is a single-frame point cloud recognition result in a measured scene; Figure 7 (c) is a single-frame point cloud suppression result in a measured scene. DETAILED DESCRIPTION

[0076] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar notations represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as a limitation of the present application. For the step numbers in the following embodiments, they are only set for the convenience of description and explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0077] In the description of the present application, it should be understood that the orientation description, such as the orientation or position relationship indicated by up, down, front, back, left, right, etc. is based on the orientation or position relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0078] In the description of the present application, the meaning of several is one or more, and the meaning of multiple is more than two, greater than, less than, more than, etc. are understood as not including the number, and above, below, etc. are understood as including the number. If it is described as first, second, it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or the order of indicated technical features.

[0079] In the description of the present application, unless otherwise explicitly limited, the words such as setting, installing, connecting, etc. should be broadly understood, and those skilled in the art can reasonably determine the specific meaning of the above words in the present application in combination with the specific content of the technical solution.

[0080] In view of the existing technical problems, the present application provides a Doppler velocity filtering multi-path false target suppression scheme based on reflection boundary fitting, which is suitable for the identification and suppression of first-order and second-order multi-path false targets caused by static reflection surfaces in millimeter wave radar detection. First, based on the characteristics of radar point cloud data, dynamic and static point cloud separation is performed, and reflection boundary fitting is performed on the static point cloud; second, a typical multi-path propagation geometric model is established, and the Doppler velocity of the first-order multi-path false target and the second-order false target is obtained respectively based on the Doppler velocity characteristics of the real target and the geometric model; finally, the dynamic point cloud is filtered based on the Doppler velocity and the multi-domain information is jointly judged, so as to realize the identification and suppression of multi-path false targets.

[0081] Figure 2 A millimeter wave radar detection scene diagram is shown in Figure 3 Figure 5 A measured scene diagram is shown. The radar O is located in the middle of the road directly above, and the target T is located between the left rail P1 and the right rail P2. Because the rail is a strong reflection surface, in addition to the direct detection signal of the target, there are multi-path false targets formed by the first and second reflection signals of the electromagnetic wave signal. Based on the reflection characteristics of the electromagnetic wave, a propagation model is established, mainly including the direct path, the first reflection of the two side rails, and the second reflection path.

[0082] The specific propagation path of the electromagnetic wave signal is:

[0083] Direct path (l1): O→T→O, the electromagnetic wave signal is transmitted from the radar O to the real target T and returned to the radar O.

[0084] First-order multi-path (l2): O→P2→T→O, the electromagnetic wave signal is transmitted from the radar O to the right rail P2, reflected to the real target T and returned to the radar O, and a first-order false target M is formed on the line connecting the radar O and the right rail P2. 21 .

[0085] First-order multi-path (l3): O→T→P2→O, the electromagnetic wave signal is transmitted from the radar O to the real target T, reflected to the right rail P2, and returned to the radar O. This path forms a first-order false target M in the same azimuth as the real target T. 22 .

[0086] Second-order multi-path (l4): O→P2→T→P2→O, the electromagnetic wave signal is transmitted from the radar O to the real target T, reflected to the right rail P2, and then reflected to the real target T, and returned to the radar O. This path produces a second-order false target S2 at the position symmetric to the real target about the right rail P2.

[0087] The specific propagation path of the electromagnetic wave signal is:​

[0088] Direct path (l5): O→T→O, the electromagnetic wave signal is transmitted from the radar O to the real target T and returns to the radar O.

[0089] First-order multipath (l6): O→P1→T→O, the electromagnetic wave signal is transmitted from the radar O to the left side rail P1, reflects to the real target T and returns to the radar O, and a first-order false target M is formed on the line connecting the radar O and the left side rail P1. 12 .

[0090] First-order multipath (l7): O→T→P1→O, the electromagnetic wave signal is transmitted from the radar O to the real target T, reflects to the left side rail P1, and returns to the radar O. This path forms a first-order false target M in the same azimuth as the real target T. 11 .

[0091] Second-order multipath (l8): O→P1→T→P1→O, the electromagnetic wave signal is transmitted from the radar O to the real target T, reflects to the left side rail P1, and then reflects to the real target T, and returns to the radar O. This path produces a second-order false target S1 at the position symmetric to the real target about the left side rail P1.

[0092] Considering the strong energy attenuation of millimeter wave radar signals after multiple reflections, the energy of high-order path signals after multiple reflections is weak and can be ignored, and only the real target, the first-order false target and the second-order false target formed by one-time and two-time reflections are considered.

[0093] Based on the modeling of the electromagnetic wave propagation signal in the above radar detection scene, the content of the embodiment of the present application is as follows.

[0094] Embodiment 1

[0095] As shown in Figure 1 , the embodiment provides a Doppler velocity domain filtering multipath false target suppression method based on reflection boundary fitting, which comprises the following steps:

[0096] S1: original radar echo signal processing.

[0097] According to the radar original echo, fast Fourier transform is performed in the fast time dimension and the slow time dimension respectively, and constant false alarm detection, angle of arrival estimation and other steps are performed to obtain the distance, Doppler velocity, angle and other information of the target, and then point cloud data is obtained.

[0098] The top view of the millimeter wave radar detection scene is shown in Figure 4 (b), assuming that the radar installation height is h, the target radial distance in the radar point cloud is R, and the radial velocity is v D', the angle between the radial distance and the radar normal is θ, and the scattering area rcs of the target, the point cloud signal-to-noise ratio snr can also be obtained. The coordinate conversion from three-dimensional space to two-dimensional space is realized for the radar point cloud, and the radial distance R in the three-dimensional space is projected into the two-dimensional space as:

[0099]

[0100]

[0101] Where r is the radial distance projected into the two-dimensional space, and α is the defined height angle.

[0102] Based on the azimuth angle θ of the radar detected target point cloud and the radial distance r projected into the two-dimensional space, the position coordinates (x, y) of the target are obtained as:

[0103]

[0104] Since the millimeter wave radar has a certain installation height h, the radial velocity v D' in the three-dimensional space is projected into the two-dimensional space as:

[0105] v D = v D' sinα

[0106] Where v D is the Doppler velocity in the two-dimensional space. Given the direction of the target motion detected by the millimeter wave radar, the motion velocity v of the target is obtained as:

[0107]

[0108] Suppose the point cloud data set is Ω, which has N point traces denoted as:

[0109] Ω = {p g | x g , y g , r g , v Dg , v g , θ g , rcs g , snr g , power g}(g = 1, 2, ……N)

[0110] Where p g is the gth point trace in the point cloud set Ω, x g , y g , r g , v Dg , v g , θ g , rcsg ,snr g ,power g (xi, yi, vi, vi, ai, snr, power) represents the position coordinates, radial velocity, motion velocity, azimuth angle, scattering area, signal-to-noise ratio, and signal power of the gth track, respectively.

[0111] S2: Moving and static point cloud separation. Based on the Doppler velocity information, the radar point cloud data obtained in step S1 is separated into static and dynamic point cloud sets.

[0112] In some embodiments, the radar point cloud set Ω obtained by step S1 is assumed to be static point cloud set S and contains L tracks, and dynamic point cloud set D and contains H tracks. Considering that there is a certain error in radar speed measurement, according to whether the Doppler velocity in the point cloud set is 0, and setting the Doppler velocity interval Δv D , the value is set to 0.3 m / s according to experience. Moving and static point cloud separation, the separation of dynamic and static point clouds is based on:

[0113]

[0114] Dynamic point cloud set D and static point cloud set S constitute point cloud set Ω:

[0115] Ω = S ∪ D

[0116] S3: Static point cloud clustering and boundary fitting. The static point cloud set is clustered based on density, and linear boundary fitting is performed to obtain the straight line equation of the reflection boundary.

[0117] Based on the static point cloud set S obtained in step S2, the DBSCAN clustering is used for the point cloud data in the set S in the embodiments of the present application, wherein the field search radius Eps is set to 4 m, and the minimum number of points in the neighborhood Minpts is set to 1. The DBSCAN processing is performed on the static point cloud of each frame, and it is assumed that M static point cloud clusters are obtained, denoted as cluster i (i = 1, 2, 3...M). The boundary fitting is performed on the data points (x i , y i ) in each static point cloud cluster cluster i . The least square method, as a common straight line detection algorithm, has certain disadvantages, and the fitting effect for a curve is not ideal, and it is sensitive to noise points. Therefore, the boundary fitting method based on the least variance Hough transform is proposed in the embodiments of the present application, which can effectively perform linear fitting of the reflection boundary.

[0118] First, coordinate conversion is performed, and the Hesse normal line is obtained for all data points (x i , y i ) in the ith cluster by using the following formula:

[0119] p = x i cos θ + y i sin θ

[0120] where p represents the perpendicular distance from the origin to the line passing through the point (x i , y i ), and θ represents the rotation angle of the perpendicular line, and according to the distribution characteristics of the static point cloud, the range of p (p min , p max ) and the range of θ (θ min , θ max ) are determined.

[0121] Secondly, a set C consisting of all curves corresponding to the data points (x i , y i ) in the cluster is obtained. In the Cartesian coordinate system, the data points approximated on a straight line intersect at a point (p j , θ j ) in the polar coordinate system, which represents the corresponding parameters of the fitted straight line.

[0122] Thirdly, the intersection point of the curve set C corresponding to the point set of each cluster is obtained. Assuming that there are m points in the i-th cluster cluster i , first, the interval (θ min , θ max ) is discretized in the polar coordinate system to obtain N discrete values, and the p of all Hesse curves under each discrete value is calculated, denoted as P (n = 1,..., N), and the variance of P is further calculated, and the angle corresponding to the minimum variance is selected as the angle estimate θ j (theoretically, the variance value at the intersection point of the Hesse curve is 0). Further, ρ j is estimated as follows:

[0123] The coordinates of each curve corresponding to the angle are recorded as p 1j , p 2j ,..., p mj , and the intersection point coordinates (θ j , p j ) of the approximate curve family are obtained:

[0124]

[0125] At this point, the intersection point coordinate estimation in the polar coordinate system (θ j , p j ) is completed.

[0126] Finally, the parameters of the fitted line are estimated. Assume that the equation of the reflecting boundary approximated by a straight line in the Cartesian coordinate system is:

[0127] Ax + By + C = 0

[0128] Based on the estimated intersection point coordinates (θ j , p j ), the Hesse normal line parameters are converted to the general straight line equation parameters:

[0129]

[0130] For all data points (x i , y i ) in the i-th cluster, the fitted line equation is obtained based on S above, and all static point cloud cluster is traversed to obtain the fitting result of each point cloud cluster, thereby achieving the estimation of the static reflecting boundary, and then the multipath false target Doppler velocity estimation is performed based on the fitted boundary.

[0131] S4: Multipath false target Doppler velocity estimation.

[0132] Referring to Figure 6 , based on the multipath propagation model, the Doppler velocity of the corresponding first-order multipath false target and the Doppler velocity of the second-order multipath false target are estimated for the target points in the dynamic point cloud, and the corresponding Doppler velocity search interval is obtained. Specifically as follows:

[0133] 1) Second-order false target Doppler velocity estimation

[0134] Assume that the coordinates of the target point in the dynamic point cloud set D are (x t , y t ), the parameters A, B, and C obtained from the straight line equation fitted in step S3 are used to calculate the coordinates (x ts , y ts ) of the second-order false target symmetrically mirrored about the straight line:

[0135]

[0136] Given the second-order false target point (x ts , y ts ) of the target point (x t , y t ) about the fitted reflecting boundary, the radial distance of the theoretical second-order false target point (x ts , y ts ) can be obtained:

[0137]

[0138] According to the differential relationship between Doppler velocity and radial distance in radar signal processing, the Doppler velocity of the theoretical second-order false target point (x ts ,y ts ) can be obtained as follows:

[0139]

[0140] Since there is a certain error in radar signal detection, the obtained Doppler velocity of the theoretical second-order false target point is set as a search interval δ1, and the threshold radius of which is 0.1 m / s.

[0141] 2) Doppler velocity estimation of first-order false target

[0142] According to the multipath propagation geometric model, the real target point trace generates first-order false targets M1 and M2 with respect to the static reflection boundary. Assuming that the coordinates of the target point trace in the dynamic point cloud set D are (x t ,y t ), the coordinates of the second-order false target obtained in 1) are (x ts ,y ts ), and the theoretical radial distance of the first-order false targets M1 and M2 obtained according to the electromagnetic wave multipath propagation model is equal, denoted as r M :

[0143]

[0144] where r ts is the theoretical radial distance of the second-order false target obtained in 1), and r t is the radial distance of the target point trace.

[0145] At the same time, the theoretical Doppler velocity of the first-order false targets M1 and M2 is obtained as follows:

[0146]

[0147] where r are the radial distances of the first-order false targets, (x t ,y t ), and (x ts ,y ts ) are the coordinates of the target point trace and the second-order false target. Since there is a certain error in radar signal detection, the obtained Doppler velocity of the theoretical first-order false targets M1 and M2 is set as a search interval δ2, and the threshold radius of which is set as 0.1 m / s.

[0148] S5: Doppler velocity filtering of dynamic point cloud. For each track in the dynamic point cloud set, step S4 is performed, and global point cloud Doppler velocity filtering is performed on the obtained search interval to obtain a dynamic point cloud set that meets the conditions.

[0149] Assuming that the dynamic target track is p i ∈D, p i ={x i ,y i ,r i ,v Di ,v i ,θ i ,rcs i ,snr i ,power i}, the radial velocity, radial distance, azimuth angle, etc. of the target track p i The position coordinates of the theoretical second-order false target S obtained by fitting the reflection boundary in step S3 Radial distance Radial velocity

[0150] Search interval and the radial distance of the first-order false target M1, M2 Radial velocity Search interval Based on the search interval , the Doppler velocity search interval δ i of the target track is obtained:

[0151]

[0152] Doppler velocity filtering is performed in the dynamic point cloud set D, and the track set that meets the search interval δ i is recorded as

[0153]

[0154] For the track p i in the dynamic point cloud set D, the track set about the point is obtained All tracks in the set satisfy the Doppler velocity constraint relationship, and the track in the set is subjected to step S6 judgment.

[0155] Since the millimeter wave radar detection has the characteristics of high resolution and wide field of view, in the processing of actual measurement data, the left and right lane targets are processed respectively according to the Doppler velocity direction of the dynamic point cloud target, and the vehicle targets on each side are geometrically modeled with the multipath propagation of the nearby road barriers.

[0156] S6: Multi-dimensional information joint decision. The original single point track in step S5 and the dynamic point cloud set meeting the condition are subjected to multi-dimensional information decision, and the dynamic point cloud track meeting the condition is regarded as a multipath false target. Steps S4-S6 are repeated to complete the multipath false target identification and suppression in all dynamic point clouds.

[0157] The multi-dimensional information decision decision maker is a system and model for processing and analyzing multi-dimensional information to make decisions and predict results for classification. Each dimension usually represents a factor or feature that affects decision making. The accuracy of the decision is improved through multi-dimensional information joint decision. The embodiment proposes a Doppler velocity filtering multipath false target suppression method based on reflection boundary fitting. The filtered point track set obtained based on the above steps includes the scattering area rcs, signal-to-noise ratio snr, and signal power power information of the point cloud. According to the detection characteristics of electromagnetic waves on targets, the target scattering area rcs, signal-to-noise ratio snr, and signal power power of the multipath false target generated by multiple reflections are much smaller than the corresponding characteristic information of the real target. Based on this feature, the dynamic point track p i The obtained Doppler velocity filtered point track set Further decision of multi-dimensional information is performed.

[0158] A multi-dimensional information decision decision maker κ is constructed, and a multi-dimensional information vector x is defined to represent n factors or features is:

[0159]

[0160] wherein x i is a certain dimension information representing data. Meanwhile, the n-dimensional weight vector corresponding to the multi-dimensional information vector is defined as is:

[0161]

[0162] wherein b i is the weight value corresponding to the i-th feature.

[0163] The output op of the decision maker can be represented as a weighted sum:

[0164]

[0165] wherein x i is a certain dimension information representing data, and b i is the weight value corresponding to the i-th feature.

[0166] Based on the millimeter wave radar detection to obtain point cloud data, including the scattering area rcs, signal-to-noise ratio snr, signal power power of the radar point cloud data point track. Define the 3D information decision decision maker, the feature vector in which is:

[0167]

[0168] Define the weight vector in the 3D information decision decision maker is:

[0169]

[0170] Where b1, b2, b3 respectively represent the weight factor corresponding to the component of the scattering area rcs, signal-to-noise ratio snr, signal power power in the vector x.

[0171] The output op of the decision maker is defined as the weighted sum, which is:

[0172]

[0173] Assume that for the point track p i in step S5 The point track in the filtered point track set

[0174] p j ={x j ,y j ,r j ,v Dj ,v j ,θ j ,rcs j ,snr j ,power j}

[0175] In this embodiment, the weight vector in the decision decision maker is valued based on empirical values, which are 0.4, 0.3, and 0.3 respectively. For the point track p i , p j uses the decision maker to obtain the output op i , op j . If the following relationship is satisfied, the point track p j in the set is considered to be a multipath false target point of the target point p i and is removed:

[0176] op i > op j

[0177] Repeat step S4 for each point in the dynamic point cloud set D to obtain the Doppler velocity estimate of the multipath false target. Based on step S5, the Doppler velocity filtering of the moving target point is completed. The obtained point set that meets the conditions is subjected to the multi-dimensional information joint judgment of step S6, thereby completing the identification and suppression of multipath false targets.

[0178] See also Figure 7 Through the measured data of millimeter-wave radar, the method provided in this embodiment is based on the multipath false targets caused by static reflection boundaries. Through filtering in the Doppler velocity dimension, it can realize the identification and suppression of multipath false targets, while ensuring that the real target is not lost, verifying the correctness and effectiveness of the present invention.

[0179] Example 2

[0180] An embodiment of the present invention further provides an electronic device, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the following Figure 1 A Doppler velocity filtering multipath false target suppression method is shown.

[0181] It is understood that the memory may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory may be used to store instructions, programs, codes, code sets, or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function, instructions for implementing the various method embodiments described above, etc.; the data storage area may store data created based on the use of the server, etc.

[0182] The processor can include one or more processing cores. The processor connects various parts within the entire server by various interfaces and lines, executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory. Alternatively, the processor can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), a programmable logic array (PLA). The processor can be integrated with one or several combinations of a central processing unit (CPU) and a modem. Among them, the CPU mainly processes operating systems and application programs; the modem is used to process wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor, but can be realized by a single chip.

[0183] Since the electronic device is an electronic device corresponding to the Doppler velocity filtering multipath false target suppression method of the embodiments of the application, and the principle of solving problems of the electronic device is similar to that of the method, the implementation of the electronic device can be referred to the implementation process of the above-mentioned method embodiments, and the repeated parts will not be described again.

[0184] Embodiment 3

[0185] The embodiments of the application also provide a computer readable storage medium, wherein at least one instruction, at least one program, a code set or an instruction set are stored in the storage medium, and the at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by a processor to realize a Doppler velocity filtering multipath false target suppression method as shown in Figure 1

[0186] ​Those skilled in the art can understand that all or part of the steps of various methods in the above embodiments can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer readable storage medium, including Read-Only Memory (ROM), Random Access Memory (RAM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), One-time Programmable Read-Only Memory (OTPROM), Electrically-Erasable Programmable Read-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM) or other optical disk memories, magnetic disk memories, magnetic tape memories, or any other computer readable medium that can be used to carry or store data.

[0187] Since the storage medium is a storage medium corresponding to the Doppler velocity filtering multipath false target suppression method of the embodiments of the present application, and the principle of solving the problem of the storage medium is similar to that of the method, the implementation of the storage medium can be referred to the implementation process of the above method embodiments, and the repeated parts will not be described again.

[0188] Embodiment 4

[0189] In some possible implementation manners, various aspects of the method of the embodiments of the present application can also be implemented in the form of a program product, which includes program codes for causing a computer device to execute the steps of the Doppler velocity filtering multipath false target suppression method according to various exemplary embodiments of the present application described above in the specification when the program product is run on the computer device. Wherein, the executable computer program codes or "codes" for executing various embodiments can be written in high-level programming languages such as C, C++, C#, Smalltalk, Java, JavaScript, Visual Basic, Structured Query Language (for example, Transact-SQL), Perl, or in various other programming languages.

[0190] It should be understood that various aspects of the application can be implemented in hardware, software, firmware or a combination of them. In the above embodiments, various steps or methods can be implemented in software or firmware which is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following technologies, known in the art, can be used to implement the hardware: discrete logic circuitry having logic gates for implementing logic functions upon data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and so forth, or a combination of them.

[0191] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Also, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. Furthermore, the person skilled in the art can combine and combine the different embodiments or examples described in the specification and the features of the different embodiments or examples, without contradiction.

[0192] The above embodiments are only for the purpose of illustrating the technical concept and characteristics of the present application, and the purpose is to enable those skilled in the art to understand the content of the present application and to implement it, and cannot limit the protection scope of the present application. Any equivalent changes or modifications made according to the essence of the present application should be covered within the protection scope of the present application.

Claims

1. A method of Doppler velocity filter multipath ghost suppression, characterized by, The method comprises the following steps: Obtaining radar point cloud data; Separating the radar original echo data into static point cloud data and dynamic point cloud data to obtain a static point cloud set and a dynamic point cloud set; Clustering the static point cloud set, fitting a linear boundary according to the clustering result, and obtaining a straight line equation of the reflection boundary; Based on a multipath propagation model, estimating the Doppler velocity of a first-order multipath false target and the Doppler velocity of a second-order multipath false target in the point track in the dynamic point cloud set respectively to obtain a corresponding Doppler velocity search interval; Traversing the dynamic point cloud set, performing global point cloud Doppler velocity filtering on the obtained search interval, and obtaining a filtered dynamic point cloud set; Performing multi-dimensional information decision on the filtered dynamic point cloud set to identify the multipath false target, so as to realize multipath false target suppression; The estimation method of the Doppler velocity of the second-order multipath false target is as follows: Assume the target point track coordinates in the dynamic point cloud set D as (x t ,y t ), the parameters A, B, C obtained based on the fitted straight line equation, and the second-order false target coordinates (x ts ,y ts ) of the target point track about the line mirror symmetry are calculated: According to the target point (x t ,y t ) about the second order false target point (x ts ,y ts ) of the fitting reflection boundary, the radial distance of the second order false target point (x ts ,y ts ) is obtained as follows: According to the differential relationship between Doppler velocity and radial distance in radar signal processing, the Doppler velocity of the second-order false target point (x ts ,y ts ) is obtained as follows: The estimation method of the Doppler velocity of the first-order multipath false target is as follows: According to the multipath propagation geometry model, the real target point trace generates first-order false targets M1, M2 about the static reflection boundary respectively; assuming that the target point trace coordinates in the dynamic point cloud set D are (x t ,y t ), the second-order false target point coordinates are (x ts ,y ts ), and the theoretical radial distance of the first-order false targets M1 and M2 obtained according to the electromagnetic wave multipath propagation model is equal, recorded as r M : where r ts is the radial distance for second order false target theory, r t is the radial distance for target point track; The theoretical Doppler velocities of the first-order false targets M1 and M2 are simultaneously obtained as follows: where (x t ,y t ), (x ts ,y ts ) are target track coordinates and second order false target coordinates; The method of obtaining the corresponding Doppler velocity search interval comprises: Assume the dynamic target track is p i ∈ D, D is a dynamic point cloud set, The target track p i The position coordinates of the theoretical second-order false target S obtained by fitting the reflection boundary Radial distance Radial velocity Search interval And the radial distance of the first-order false target M1, M2 Radial velocity Search interval Based on the search interval The Doppler velocity search interval δ of the target track is obtained i Is: The method of performing global point cloud Doppler velocity filtering on the obtained search interval to obtain a filtered dynamic point cloud set comprises: Doppler velocity filtering is performed in the dynamic point cloud set D, such that the point track set i is denoted by For a point track p in the dynamic point cloud set D i , the point track set P satisfying the above conditions is obtained All point tracks in the set satisfy the constraint relationship of Doppler velocity.

2. The method of claim 1, wherein, The expression of the radar point cloud data is as follows: where p g is the gth point trail in the point cloud set Ω; x g , y g denote the position coordinates of the gth point trail; r g denotes the radial distance of the gth point trail; denotes the Doppler velocity of the gth point trail; v g denotes the motion velocity of the gth point trail; θ g denotes the azimuth angle of the gth point trail; rcs g denotes the scattering area of the gth point trail; snr g denotes the signal-to-noise ratio of the gth point trail; power g denotes the signal power of the gth point trail; and N is the number of point trails.

3. The method of claim 1, wherein, The method of clustering the static point cloud set, fitting a linear boundary according to the clustering result, and obtaining a straight line equation of the reflection boundary comprises: A density-based clustering method is used to cluster the static point cloud set, and a Hough transform is used to fit the reflection boundary in each clustering cluster to obtain a straight line equation of the reflection boundary.

4. The method of claim 3, wherein, The method of clustering the static point cloud set using a density-based clustering method and fitting the reflection boundary in each clustering cluster using a Hough transform comprises: DBSCAN processing is performed on the static point cloud of each frame to obtain M static point cloud clusters; The Hessian normal of all data points (x i ,y i ) in the ith cluster is obtained by the following formula: p = x i cos θ + y i sin θ where p represents the perpendicular distance from the origin to the line passing through the point (x i ,y i ), and θ represents the rotation angle of the perpendicular line; All curve sets C corresponding to the data points in the clustering cluster are obtained; estimating intersection points (θ, p) of the curve set C corresponding to the point set of each cluster j , j ); Finally, parameter estimation is performed on the fitted straight line: Assuming that the fitted straight line equation of the reflection boundary in a Cartesian coordinate system is as follows: Ax+By+C=0 Based on the estimated intersection point coordinates (θ j ,ρ j ), the Hesse normal line parameters are converted to general straight line equation parameters: For all data points (x i ,y i ) in the i-th cluster, based on the fitting straight line equation, traverse all static point cloud cluster, get the fitting result of each point cloud cluster, so as to realize the estimation of static reflection boundary.

5. The method of claim 1 wherein, Multi-dimensional information decision is realized by constructing a multi-dimensional information decision discriminator, and the specific method is as follows: A 3D feature vector is defined as follows: Wherein, rcs is the scattering area of the point cloud data, snr is the signal-to-noise ratio of the point cloud, and power is the signal power. Defining weight vectors in 3-dimensional information decision deciders is: The output op weighted sum of the decision discriminator is calculated as follows: The decision discriminator is used to calculate the output of the dynamic target point cloud track and the point cloud set track after Doppler velocity filtering, so as to realize the identification of the multipath false target.

6. An electronic device, comprising: The electronic device comprises a processor and a memory, and the memory stores at least one instruction, at least one program, a code set or an instruction set, which are loaded and executed by the processor to realize the method according to any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction, at least one program, a code set or an instruction set, which are loaded and executed by the processor to realize the method according to any one of claims 1 to 5.

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