A radar trace aggregation method based on interval expansion and merging

Abstract

The present invention discloses a radar trace aggregation method based on interval expansion and merging, comprising the following steps: performing distance dimension aggregation on original trace data: performing distance dimension merging and aggregation on the original trace data in the same azimuth; initializing interval block structure parameters: after completing distance dimension aggregation and when the number of interval blocks is empty, initializing the new traces in the current batch as new interval blocks; iteratively associating and updating the new traces with interval blocks; trace contention and near-target resolution: handling conflicts by combining the existing interval block centroid and trace feature parameters; centroid aggregation of data within the interval block: performing final aggregation on the interval internal data that meets the aggregation conditions; trace filtering: filtering out non-target traces based on the resulting features of the aggregated traces. The present invention effectively solves the problem of insufficient aggregation caused by the azimuth sliding window method from the perspective of intervals, achieves good trace filtering effects, and meets the real-time requirements of radar data preprocessing while ensuring performance.

Description

Technical Field

[0001] The present invention relates to the technical field of radar data processing, and in particular to a radar trace aggregation method based on interval extension and merging. Background Art

[0002] With the rapid development of various fields such as semiconductors and computer technology, radar research has achieved significant breakthroughs, and its application areas and research content are constantly expanding. Modern radar not only plays a vital role in the military but is also commonly used in the national economy and other fields. Due to the low resolution of early radars, the raw target information detected after radar signal processing consisted of only one or a few points, and the target point condensation processing required relatively little computation. With the advancement of radar imaging technology and the improvement of radar measurement accuracy, the amount of valid target information received by the radar data processing end has increased exponentially. However, ideal radar data processing results require only one point per target in a single scan. While this large amount of valid information facilitates more accurate target measurement, it also places higher demands on the point condensation algorithm.

[0003] The University of Electronic Science and Technology of China disclosed a target detection point trace aggregation method based on peak clustering in its patent application document "A target detection point trace aggregation method based on peak clustering" (patent application number: 202110538477.5, application publication number: CN113269242A). The algorithm takes into account the characteristics of data points of the same category in the CFAR detection point trace results that cluster towards the peak points, and designs a clustering algorithm that automatically finds peak points. The Euclidean distance of all collected data is calculated based on the distance-Doppler two-dimensional information, and the amplitude information is used to find the peak data points of each category and then cluster them. It can overcome the difficulty of correctly classifying multiple targets that are adjacent to each other and have large differences in point trace data amplitude and density distribution.

[0004] Beijing Zhongan Ruida Electronic Technology Co., Ltd. disclosed a method for primary point trace aggregation based on a sector structure in its patent application document "A method for primary point trace aggregation based on a sector structure" (patent application number: 202111217802.4, application publication number: CN113820685A). It is suitable for the tracking while scanning (TWS) working mode. Taking the azimuth sector and the distance sector as the basic units, for the primary point trace of a certain distance sector in a specific azimuth sector, the point trace is first aggregated with the remaining primary point traces of the current distance sector; then, the point trace is aggregated with all the primary point traces in the previous distance sector of the current distance sector in the current azimuth sector; finally, the point trace is aggregated with all the primary point traces in the corresponding distance sector and the previous distance sector of the corresponding distance sector in several azimuth sectors before the current azimuth sector. This invention can effectively avoid invalid matching between primary point traces with large distance differences, but the repeated judgment between multiple azimuth sectors and distance sectors still increases the complexity of point trace aggregation, which is not conducive to further improvement of the algorithm computational complexity.

[0005] Xi'an Coresheng Innovation Technology Co., Ltd., in its patent application, "Method and System for Sea Surface Target Point Trace Agglomeration Based on Contour Tracking and Region Growing" (Patent Application No.: 202011602419.6, Publication No.: CN112630743A), discloses a method and system for sea surface target point trace aggregation based on contour tracking and region growing. The method comprises: acquiring a sea surface image and performing binarization and morphological preprocessing on the image; detecting and tracking each sea surface target in the image using contour tracking technology; then using region growing to obtain the regional pixel points of each sea surface target; and performing point trace aggregation on the regional pixel points of each target to obtain the final aggregation result. This method can obtain a more accurate target aggregation position and improve the accuracy of point trace aggregation. The detection and region growing are mainly implemented in a two-dimensional grid through two-dimensional search and neighborhood traversal methods. The grayscale value of each grid pixel is traversed and judged multiple times. While achieving high-precision aggregation also brings a certain amount of time. In the field of sea surface imaging, imaging always indicates a certain amount of data accumulation, and the problem of point trace aggregation at the edge of the imaging data has always been difficult to process.

[0006] In summary, the traditional point trace aggregation algorithm uses an azimuth sliding window to determine the target's azimuth information. It then performs point aggregation in the range dimension to obtain the current target information, including the target's range center, target amplitude, azimuth angle, and pitch angle. The main idea is to accumulate data by setting a certain azimuth sliding window width and then internally aggregate the accumulated data when the accumulation conditions are met. However, for large targets or high-azimuth-resolution radars, target detection traces may be exactly divided by the window edge or span multiple window widths. Since two-coordinate early warning radars lack azimuth angle measurement, their azimuth beamwidth is very narrow. Therefore, the antenna pointing is used as the azimuth angle of a single target detection. The original point traces are split in azimuth based on the radar's azimuth resolution, resulting in severe azimuth splitting. Furthermore, due to the cyclic scanning nature of radars, the scan angle of a moving target during one radar rotation is consistent with the antenna's half-power beamwidth. Therefore, the sliding window method cannot be used for azimuth aggregation when the condensed point trace is required to have only one point.

[0007] Another type of agglomerative method based on the DBSCAN clustering concept, compared to traditional agglomerative algorithms, can cluster the raw data in the entire polar coordinate system after one cycle of accumulation, overcoming the sliding window problem. Its main idea is to recursively calculate the Euclidean distance between any two points, group all points that meet the Euclidean distance threshold into a cluster, and then perform point agglomeration within the cluster. However, due to the azimuth splitting nature of the two-coordinate early warning radar system, the Euclidean distance between two close-range points and the Euclidean distance between two far-range points at the same azimuth resolution can differ significantly. A too small agglomeration threshold can easily lead to failure at long distances, while a too large threshold can easily lead to false agglomeration of close-range or adjacent targets. Summary of the Invention

[0008] To address these issues, the present invention considers the operating characteristics and trace features of two-coordinate early warning radars and proposes a radar trace aggregation method based on interval expansion and merging. By applying this approach from an interval perspective, the present invention effectively addresses the insufficient aggregation problem associated with the azimuth sliding window method. The resulting trace filtering achieves excellent results, ensuring performance while meeting the real-time requirements of radar data preprocessing.

[0009] The technical solution adopted in the present invention is as follows:

[0010] A radar trace aggregation method based on interval expansion and merging includes the following steps:

[0011] (1) Perform distance dimension aggregation on the original point trace data: perform distance dimension merging and aggregation on the original point trace data in the same direction;

[0012] (2) Initialize the interval block structure parameters: After completing the distance dimension condensation, and when the number of interval blocks is empty, initialize the new points in the current batch as a new interval block;

[0013] (3) Iteratively associate and update the new point traces and interval blocks: try to associate the new point traces with the distance and azimuth information of the original interval parameters, add the distance dimension condensation points that meet the conditions to the corresponding interval block, and expand and update the range parameters of the interval block;

[0014] (4) Point contention and near-target discrimination: For interval blocks with overlap, contention, or adjacent small targets, the conflict situation is handled by combining the existing interval block centroid and point feature parameters;

[0015] (5) Centroid aggregation of internal data of interval blocks: final aggregation of internal data of intervals that meet the aggregation conditions;

[0016] (6) Point trace filtering: Filter out non-target points based on the resulting features of the condensed points.

[0017] Furthermore, in step (1), after receiving the original detection results from the target detection module of the radar signal processor, the distance and echo amplitude information of each original point trace in the current batch of results are extracted, and the distance dimension is condensed once using the two-point sliding window method. The data of the original point trace includes the recording time, distance, Doppler velocity, azimuth and echo amplitude of the point trace.

[0018] Furthermore, the use of the two-point sliding window method to perform distance dimension primary condensation includes the following steps:

[0019] (1a) Sort the directly received original traces in ascending order of distance information;

[0020] (1b) Traverse the sorted trace data sequentially and calculate the distance difference between two adjacent target points. When the adjacent difference meets the cohesive distance threshold condition, they are classified as the same target. When the adjacent difference does not meet the distance threshold, the latest point in the two-point sliding window is regarded as the new target, and continue to judge backward based on this, dividing the distance dimension trace into a series of one-dimensional trace blocks.

[0021] (1c) Perform a point condensation on the divided point blocks.

[0022] Furthermore, in step (2), a two-dimensional coordinate system is established with the azimuth angle as the horizontal axis and the radial distance between the target and the radar as the vertical axis. The shape of the interval block in the two-dimensional coordinate system is a rectangular block with an azimuth width and a distance width. The interval block parameters include the starting azimuth angle Azi of the interval start 、End azimuth Azi end , starting distance R min , termination distance R max , the list of points to be condensed, and the number of all original points contained in the interval block when it meets the condensation conditions.

[0023] Furthermore, in step (3), the method for associating the new point traces and the interval blocks includes the following steps:

[0024] (3a) Perform feature analysis on the original interval blocks and distance dimension condensed points. For the M interval blocks, ensure that they are sorted from small to large according to the interval starting distance information, and sort the N distance dimension condensed points according to the distance information.

[0025] (3b) When two types of data are traversed in parallel, for the i-th interval block and the j-th distance dimension condensation point, when the difference between the interval starting distance and the condensation point distance is greater than the distance condensation threshold, jump to the next distance point and continue to perform association judgment on the i-th interval block and the j+1-th distance point; and when the difference between the interval ending distance and the distance to the point to be associated is greater than the distance condensation threshold, continue to traverse the next interval, that is, perform association judgment on the i+1-th interval block and the j-th distance point; in other cases, determine that the current point trace can be associated with the interval block, add the target point to the point trace sequence inside the interval block, and update the boundary parameters of the interval at the same time, and then continue to judge the i+1-th interval block and the j+1-th point to be associated, and repeat the iteration until all data are traversed.

[0026] Furthermore, in step (4), during the traversal process of interval association judgment, the conflict of competing for a single point being associated with multiple interval blocks is resolved, and the adjacent targets of the interval blocks that gradually overlap or have an overlapping trend during the expansion process are distinguished to complete the correct association of each point data. Finally, the remaining point data each form a new interval block structure and participate in the association traversal when the next round of new points arrive.

[0027] Furthermore, step (4) includes the following sub-steps:

[0028] (4a) For the interval block parameters involved in the point trace contention and near target overlap phenomenon, calculate the temporary centroid results of each interval block according to the centroid formula, including the distance, direction, and amplitude centroid information of the current point trace list data;

[0029] (4b) For the point trace contention phenomenon, the positional relationship between the temporary centroids of multiple intervals and the point traces to be associated is determined, and the nearest interval block is taken as the final associated object of the point trace; for adjacent intervals, the position and amplitude feature information of the corresponding temporary centroids are compared. When the temporary centroids meet the point trace aggregation threshold, the two interval blocks are merged and determined to be interval point traces of the same target; otherwise, they are determined to be two adjacent small targets, and the expansion and aggregation are continued based on the temporary centroids.

[0030] Furthermore, in step (5), according to the direction of the radar antenna, for the interval blocks that meet the condensation conditions, the amplitude weighted method is used to perform secondary condensation on the internal data, and the characteristic parameters of the final target point are extracted.

[0031] Furthermore, in step (5), when the angle difference between the antenna azimuth pointing angle and the interval end angle is greater than the azimuth condensation threshold, the interval block meets the condensation condition, and the corresponding judgment formula is:

[0032] (Azi1+360-Azi0)%360≥Azi num *Azi pix

[0033] Where Azi1 represents the azimuth angle of the current antenna center, Azi0 represents the end azimuth angle of the interval block, and Azi num Indicates the number of condensation thresholds of the azimuth unit when condensing the azimuth dimension. pix Indicates the azimuth resolution of the radar system.

[0034] Furthermore, in step (6), the final condensed point trace data is combined with the splitting characteristics of the target point in the radar system to set the corresponding point trace splitting number, distance width, azimuth width and amplitude threshold to filter out non-target points.

[0035] The beneficial effects of the present invention are:

[0036] 1. When performing the first condensation of the distance dimension, the present invention adopts a two-point sliding window merging method, which only requires calculating the distance difference between adjacent point traces. Each point trace is traversed at most twice. Compared with other methods, the number of traversals for peak retrieval of all distance units is greatly reduced, thereby improving data processing performance.

[0037] 2. The present invention adopts an associated method of interval block expansion and merging when performing azimuth-dimensional aggregation, and comprehensively considers the two-dimensional information of distance and azimuth during secondary aggregation. Compared with the existing technology that only processes point traces of different azimuths at the same distance during azimuth aggregation, the efficiency and fault tolerance of point trace merging are greatly improved. At the same time, the method of detection and aggregation is adopted to effectively suppress the splitting phenomenon caused by sliding window data accumulation, which can effectively solve the problems of point trace competition and adjacent target resolution.

[0038] 3. Based on point trace aggregation, the present invention further performs point trace filtering processing, comprehensively considering the various characteristics of the final target point trace, and can effectively identify and filter out discrete noise points and points in dense clutter areas, thereby improving the accuracy of point trace processing.

[0039] 4. The point trace objects studied by the existing point trace aggregation technology are mostly based on the radar detection system after azimuth and elevation angle measurement. The main error of the original point trace data during point trace aggregation comes from the error of the angle measurement itself, and the distribution of the point traces is still mainly spatial random errors centered on the real point traces; the research object targeted by the present invention is mainly two-coordinate early warning radar, which can be applied to various models of radars under the same system.

[0040] 5. Traditional point trace aggregation technology mainly uses a sliding window accumulation method to accumulate data within a certain period of time before performing point trace aggregation. The edge traces of the sliding window are prone to splitting and the processing results have a certain lag. The present invention uses a two-dimensional interval expansion and merging aggregation method, which no longer requires data accumulation and effectively solves the problems of insufficient aggregation and lag. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a flow chart of the radar trace condensation method based on interval expansion and merging of the present invention.

[0042] Figure 2 It is a schematic diagram of the coagulation process of the present invention. DETAILED DESCRIPTION

[0043] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, the specific embodiments of the present invention are now described. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. That is, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0044] like Figure 1 As shown, this embodiment provides a radar trace aggregation method based on interval extension and merging, including the following steps:

[0045] (1) Perform distance dimension aggregation on the original point trace data: perform distance dimension merging and aggregation on the original point trace data in the same direction;

[0046] (2) Initialize the interval block structure parameters: After completing the distance dimension condensation, and when the number of interval blocks is empty, initialize the new points in the current batch as a new interval block;

[0047] (3) Iteratively associate and update the new point traces and interval blocks: try to associate the new point traces with the distance and azimuth information of the original interval parameters, add the distance dimension condensation points that meet the conditions to the corresponding interval block, and expand and update the range parameters of the interval block;

[0048] (4) Point contention and near-target discrimination: For interval blocks with overlap, contention, or adjacent small targets, the conflict situation is handled by combining the existing interval block centroid and point feature parameters;

[0049] (5) Centroid aggregation of internal data of interval blocks: final aggregation of internal data of intervals that meet the aggregation conditions;

[0050] (6) Point trace filtering: Filter out non-target points based on the resulting features of the condensed points.

[0051] Preferably, in step (1), after receiving the original detection results from the target detection module of the radar signal processor, the distance and echo amplitude information of each original point trace in the current batch of results are extracted, and a two-point sliding window method is used to perform a primary condensation in the distance dimension. More preferably, the data of the original point trace includes the point trace recording time, distance, Doppler velocity, azimuth, and echo amplitude.

[0052] Preferably, using the two-point sliding window method to perform distance dimension primary condensation includes the following steps:

[0053] (1a) Sort the directly received original traces in ascending order of distance information;

[0054] (1b) Traverse the sorted trace data sequentially and calculate the distance difference between two adjacent target points. When the adjacent difference meets the cohesive distance threshold condition, they are classified as the same target. When the adjacent difference does not meet the distance threshold, the latest point in the two-point sliding window is regarded as the new target, and continue to judge backward based on this, dividing the distance dimension trace into a series of one-dimensional trace blocks.

[0055] (1c) Perform a point condensation on the divided point blocks. The specific calculation formula is as follows:

[0056] A′=max(A0,A1,A2,…,A N-1 ,A N )

[0057]

[0058] Where A′ represents the maximum amplitude of the same set of target points when the distance dimension is condensed, and A i represents the echo amplitude of each target point in the group, N represents the number of points merged into the same target in the distance dimension, R′ represents the distance-weighted centroid of the target point traces in the group, and R i Indicates the distance information of each target point.

[0059] Preferably, in step (2), the number of existing interval blocks in the condensation processing system needs to be determined. If the number is not empty, the association between the new point trace and the original interval block can be continued. If the number is empty, the new point trace is directly initialized as a new interval block object.

[0060] Considering that in the clockwise sweep search mode of the radar, the azimuth angle starts from 0 degrees, rotates to 360 degrees, and then starts again from 0 degrees, and continues to rotate in a continuous cycle. Preferably, a two-dimensional coordinate system is established with the azimuth angle as the horizontal axis and the radial distance between the target and the radar as the vertical axis. The shape of the interval block in the two-dimensional coordinate system is a rectangular block with an azimuth width and a distance width. The interval block parameters include the starting azimuth angle Azi of the interval start 、End azimuth Azi end , starting distance R min , termination distance R max , the list of points to be condensed, and the number of all original points contained in the interval block when it meets the condensation conditions.

[0061] Preferably, in step (3), the method for associating the new point traces with the interval blocks includes the following steps:

[0062] (3a) Perform feature analysis on the original interval blocks and distance dimension condensed points. For the M interval blocks, ensure that they are sorted from small to large according to the interval starting distance information, and sort the N distance dimension condensed points according to the distance information.

[0063] (3b) When two types of data are traversed in parallel, for the i-th interval block and the j-th distance dimension condensation point, when the difference between the interval starting distance and the condensation point distance is greater than the distance condensation threshold, jump to the next distance point and continue to perform association judgment on the i-th interval block and the j+1-th distance point; and when the difference between the interval ending distance and the distance to the point to be associated is greater than the distance condensation threshold, continue to traverse the next interval, that is, perform association judgment on the i+1-th interval block and the j-th distance point; in other cases, determine that the current point trace can be associated with the interval block, add the target point to the point trace sequence inside the interval block, and update the boundary parameters of the interval at the same time, and then continue to judge the i+1-th interval block and the j+1-th point to be associated, and repeat the iteration until all data are traversed.

[0064] Preferably, in step (4), during the traversal process of interval association judgment, the conflict of competing for a single point trace to be associated with multiple interval blocks is resolved, and the adjacent target is identified for the interval blocks that gradually overlap or have an overlapping trend during the expansion process, so as to complete the correct association of each point trace data. Finally, the remaining point trace data each form a new interval block structure and participate in the association traversal when the next round of new point traces arrive.

[0065] Preferably, step (4) includes the following sub-steps:

[0066] (4a) For the interval block parameters involved in the point trace contention and near target overlap phenomenon, calculate the temporary centroid results of each interval block according to the centroid formula, including the distance, direction, and amplitude centroid information of the current point trace list data;

[0067] (4b) For the point trace contention phenomenon, the positional relationship between the temporary centroids of multiple intervals and the point traces to be associated is determined, and the nearest interval block is taken as the final associated object of the point trace; for adjacent intervals, the position and amplitude feature information of the corresponding temporary centroids are compared. When the temporary centroids meet the point trace aggregation threshold, the two interval blocks are merged and determined to be interval point traces of the same target; otherwise, they are determined to be two adjacent small targets, and the expansion and aggregation are continued based on the temporary centroids.

[0068] Preferably, in step (5), according to the direction of the radar antenna, for the interval blocks that meet the condensation conditions, an amplitude weighted method is used to perform secondary condensation on the internal data, and the characteristic parameters of the final target point are extracted.

[0069] Preferably, in step (5), when the relationship between the interval block termination azimuth angle Azi0 and the antenna scanning pointing angle Azi1 satisfies the associated termination condition, the condensation operation of the internal data of the interval block can be performed to obtain the final point trace condensation result. More preferably, the termination condition and condensation formula are:

[0070] (Azi1+360-Azi0)%360≥Azi num *Azi pix

[0071]

[0072]

[0073]

[0074]

[0075] Where Azi num Indicates the number of condensation thresholds of the azimuth unit when condensing the azimuth dimension. pix represents the azimuth resolution of the radar system, k is the number of traces in the trace list, A i ′、R i ′、α i are the amplitude, distance, recording time and azimuth information of the i-th point in the list, A, R, t, α are the corresponding centroid estimates, N i is the distance dimension splitting number of the i-th point trace, and N is the total splitting number of the point trace in the interval block.

[0076] Preferably, in step (6), the condensed point trace data finally obtained is combined with the splitting characteristics of the target point in the radar system to set the corresponding point trace splitting number, distance width, azimuth width and amplitude threshold to filter out non-target points.

[0077] like Figure 2As shown, the plane schematic effects of various situations of the point trace condensation process in the present invention are described, including seven situations p1 to p7:

[0078] Cases p1 and p2 represent the initialization state of the interval block. At this time, the interval block has just been created and there is only one point trace inside.

[0079] Cases p3 and p4 represent the schematic diagram of the interval block structure during the condensation process. At this time, the interval block contains multiple points, and its interval boundary is also expanded accordingly, with a certain azimuth width and distance width;

[0080] Case p5 indicates that the interval blocks overlap or have an overlapping trend during the condensation process. This may be a large clutter area caused by terrain or a nearby small target. The temporary centroid parameters can be calculated based on the current internal point list. The position and amplitude feature information of the two temporary centroids can be compared to determine whether the interval blocks can be merged.

[0081] Case p6 represents the point contention phenomenon in the condensation process. At this time, both interval blocks can be associated with the new point, and it is necessary to determine the straight-line distance between the new point and the two temporary centroids.

[0082] Case p7 represents the judgment of the association termination condition. The azimuth angle Azi1 represents the azimuth angle of the antenna beam center, and Azi0 represents the ending azimuth angle of interval block p4. When the angle difference between the two is less than the azimuth condensation threshold, it can be determined that the interval block has not been updated for a long time and the condensation condition of the internal point trace has been met.

[0083] It should be noted that, for the sake of simplicity, the aforementioned method embodiments are described as a series of action combinations. However, those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

Claims

1. A radar trace aggregation method based on interval expansion and merging, characterized in that: The steps include: (1) Perform distance dimension aggregation on the original point trace data: Merge and aggregate the original point trace data in the same direction in the distance dimension; (2) Initialize the interval block structure parameters: After completing the distance dimension condensation, and when the number of interval blocks is empty, initialize the new points in the current batch as a new interval block; (3) Iteratively associate and update the new points and interval blocks: try to associate the new points with the distance and azimuth information of the original interval parameters, add the distance dimension condensation points that meet the conditions to the corresponding interval block, and expand and update the range parameters of the interval block; (4) Point contention and near-target resolution: For interval blocks with overlap, contention, or adjacent small targets, the conflict situation is handled by combining the existing interval block centroid and point feature parameters; (5) Centroid aggregation of internal data of interval blocks: Final aggregation of internal data of intervals that meet the aggregation conditions; (6) Point trace filtering: Filter out non-target points based on the resulting features of the condensed points; Step (4) includes the following sub-steps: (4a) For the interval block parameters involved in the point track contention and near target overlap phenomenon, calculate the temporary centroid results of each interval block according to the centroid formula, including the distance, direction, and amplitude centroid information of the current point track list data; (4b) For the point trace contention phenomenon, the positional relationship between the temporary centroids of multiple intervals and the point traces to be associated is determined, and the nearest interval block is taken as the final associated object of the point trace; for adjacent intervals, the position and amplitude feature information of the corresponding temporary centroids are compared. When the temporary centroids meet the point trace aggregation threshold, the two interval blocks are merged and determined to be interval point traces of the same target; otherwise, they are determined to be two adjacent small targets, and the expansion and aggregation are continued based on the temporary centroids.

2. The radar trace aggregation method based on interval expansion and merging according to claim 1, characterized in that: In step (1), after receiving the original detection results from the target detection module of the radar signal processor, the distance and echo amplitude information of each original point trace in the current batch of results are extracted, and the distance dimension is condensed once using the two-point sliding window method. The data of the original point trace includes the recording time, distance, Doppler velocity, azimuth and echo amplitude of the point trace.

3. The radar trace aggregation method based on interval expansion and merging according to claim 2, characterized in that: The use of the two-point sliding window method to perform distance dimension primary condensation includes the following steps: (1a) Sort the directly received original traces in ascending order of distance information; (1b) Traverse the sorted trace data sequentially and calculate the distance difference between two adjacent target points. When the adjacent difference meets the cohesive distance threshold condition, they are classified as the same target. When the adjacent difference does not meet the distance threshold, the latest point in the two-point sliding window is regarded as the new target, and continue to judge backward based on this, dividing the distance dimension trace into a series of one-dimensional trace blocks. (1c) Perform a point condensation on the divided point blocks.

4. The radar trace aggregation method based on interval expansion and merging according to claim 1, characterized in that: In step (2), a two-dimensional coordinate system is established with the azimuth angle as the horizontal axis and the radial distance between the target and the radar as the vertical axis. The shape of the interval block in the two-dimensional coordinate system is a rectangular block with an azimuth width and a distance width. The interval block parameters include the starting azimuth angle of the interval , end azimuth , starting distance , end distance , the list of points to be condensed, and the number of all original points contained in the interval block when it meets the condensation conditions.

5. The radar trace aggregation method based on interval expansion and merging according to claim 1, characterized in that: In step (3), the method for associating the new point traces and the interval blocks includes the following steps: (3a) Perform feature analysis on the original interval blocks and distance dimension condensed points. M interval blocks, ensuring that they are sorted from small to large according to the interval starting distance information, N The distance dimension condensation points are sorted by distance information; (3b) When two types of data are traversed in parallel, i interval blocks and j When the difference between the starting distance of the interval and the distance of the condensation point is greater than the distance condensation threshold, jump to the next distance point. i interval blocks and When the difference between the interval end distance and the distance to be associated point is greater than the distance condensation threshold, continue to traverse the next interval, that is, for the first interval blocks and j In other cases, the current point trace is determined to be associated with the interval block, the target point is added to the point trace sequence inside the interval block, and the boundary parameters of the interval are updated at the same time, and then the first interval blocks and The points to be associated are judged and the iteration is repeated until all the data are traversed.

6. The radar trace aggregation method based on interval expansion and merging according to claim 1, characterized in that: In step (4), during the traversal process of interval association judgment, the conflict of competing for a single point being associated with multiple interval blocks is resolved, and the adjacent targets of the interval blocks that gradually overlap or have an overlapping trend during the expansion process are distinguished to complete the correct association of each point data. Finally, the remaining point data each form a new interval block structure and participate in the association traversal when the next round of new points arrive.

7. The radar trace aggregation method based on interval expansion and merging according to claim 1, characterized in that: In step (5), according to the direction of the radar antenna, the internal data of the interval blocks that meet the condensation conditions are condensed twice using the amplitude weighting method, and the characteristic parameters of the final target point are extracted.

8. The radar trace aggregation method based on interval expansion and merging according to claim 1, characterized in that: In step (5), when the angle difference between the antenna azimuth pointing angle and the interval end angle is greater than the azimuth condensation threshold, the interval block meets the condensation condition, and the corresponding judgment formula is: Where, Indicates the azimuth direction of the current antenna center. Indicates the end azimuth of the interval block, Indicates the number of condensation thresholds of the orientation units when condensing the orientation dimension. Indicates the azimuth resolution of the radar system.

9. The radar trace aggregation method based on interval expansion and merging according to any one of claims 1 to 8, characterized in that: In step (6), the final condensed point trace data is combined with the splitting characteristics of the target points in the radar system to set the corresponding point trace splitting number, distance width, azimuth width and amplitude threshold to filter out non-target points.

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