A search radar echo plot condensation processing method

Abstract

The application discloses a search radar echo track condensation processing method, which comprehensively considers echo characteristics of split targets and small targets, sets corresponding thresholds when processing original echo data, and then adopts a centroid method to perform condensation processing on the data over the threshold from a distance unit, a pitch unit and an azimuth unit, so that the small targets can be avoided from being filtered out and the split phenomenon of the targets is considered. That is, the application is based on an existing centroid method for azimuth, quantizes echo data according to the distance unit, the pitch unit and the azimuth unit one by one, then condenses the distance unit, the pitch unit and the azimuth unit in sequence, and then gradually processes the original echo data, and finally outputs accurate track parameters. The test results show that the algorithm can accurately distinguish adjacent targets in different azimuths or distances, and avoids forming multiple observation points in the distance or azimuth of the same target.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing technology, and in particular to a method for processing radar echo point clusters. Background Technology

[0002] Currently, there are two main methods for target aggregation processing. The centroid method analyzes the collected data and then successively merges the data across multiple range quantization units and adjacent azimuth sectors to output reliable target parameters. Beam enlargement, on the other hand, is primarily used in multi-beam elevation scenarios. Within the same range unit, it compares the signal amplitudes of different elevation beams that exceed a preset threshold, and takes the beam containing the peak value of the echo signal amplitude envelope of each transmitted beam as the target center. Selecting this beam center yields the target's elevation angle parameters. Within a certain elevation angle range, the probability of two groups of targets at different altitudes appearing simultaneously in the same range unit is extremely low. In scenarios where the same transmitted beam is used, a group of receiving beams will at most aggregate only one target. However, existing centroid-based azimuth aggregation algorithms suffer from incomplete target information extraction, only considering target splitting in the azimuth and ignoring the effects of target elevation clutter and splitting. Existing beam enlargement methods also ignore target splitting, resulting in relatively absolute target point extraction and significant extraction errors. Furthermore, because it removes a large amount of splitting information, some weak targets are easily overlooked, resulting in a rather one-sided extraction of target parameters. Summary of the Invention

[0003] This invention provides a method for processing radar echo point clusters to solve the problem of inaccuracy in existing radar echo point cluster processing methods.

[0004] This invention provides a method for processing radar echo point clustering. The method includes: setting an echo amplitude threshold based on the echo characteristics of split targets and weak targets; for raw echo data exceeding the echo amplitude threshold, firstly clustering different range units of the raw echo data at the same azimuth and elevation; after the range dimension clustering of each elevation unit at the same azimuth is completed, storing the range dimension clustering result of each elevation as the range dimension clustering result; then using the range dimension clustering result to cluster each elevation dimension at a certain azimuth, and storing the elevation dimension clustering result at the current azimuth; after the clustering of each elevation dimension at that azimuth is completed, starting from the current azimuth plane, sequentially performing range and elevation dimension clustering on the next azimuth; when traversing to the Nth azimuth, outputting the clustered point information.

[0005] Optionally, the method further includes: setting the maximum concentration range cell, the minimum concentration range cell, the maximum number of concentration elevation layers, the minimum number of elevation concentration layers, the maximum number of concentration azimuth layers, and the minimum number of azimuth concentration layers according to the transmit beam characteristics of the searched radar;

[0006] Simultaneously, the maximum interruption interval threshold of the continuous distance unit corresponding to distance cohesion, the maximum interruption interval of the left and right continuous distance units during pitch cohesion, and the maximum interruption interval of the left and right continuous distance units during azimuth cohesion were determined.

[0007] Optionally, the agglomeration of different distance cells in the same azimuth and elevation dimensions of the original echo data includes: traversing the distance cells that have continuously exceeded the threshold, determining the number of consecutive interrupted cells that have not exceeded the echo amplitude threshold during the traversal, and if the number of consecutive interrupted cells is greater than the preset maximum interruption interval threshold, then the traversal of this agglomeration block is terminated, the centroid of the distance agglomeration block traversed in this traversal is calculated to obtain the agglomeration result of this block, and then the starting number of the distance cell for the next traversal is updated, and a new agglomeration block is started to continue agglomeration; otherwise, the traversal is continued by adding 1 to the current traversed distance cell, and if the current number of agglomeration cells is greater than the preset maximum agglomeration distance cell, then the agglomeration of the distance cells in the current elevation layer is completed, and the agglomeration results of the distance dimension of all agglomeration blocks are saved.

[0008] Optionally, the step of calculating the centroid of the distance agglomerates during this traversal to obtain the agglomeration result of the agglomerate includes:

[0009] According to the distance condensation formula The centroid of the distance-bound condensate block is calculated to obtain the condensation result of this block; where R represents the distance, R i A represents the distance of the echo signal from the i-th point. i The amplitude of the echo signal of the i-th point is represented, and n represents the number of processed points in the agglomerate.

[0010] Optionally, the step of using the distance dimension aggregation result to aggregate each pitch dimension at a certain azimuth and storing the pitch dimension aggregation result at the current azimuth includes: determining whether the current pitch echo is at the last layer; if so, using the distance dimension aggregation result of the first layer of pitch to start traversing to find the pitch aggregation block; during the traversal, starting from the traversal point DisResult[i][j], traversing to the left and right continuous distance units respectively, where i is the pitch layer number and j is the current distance unit; if the number of left continuous interruption units that have not exceeded the echo amplitude threshold is greater than the preset maximum interruption interval of the left continuous distance unit, the left traversal starting from DisResult[i][j] ends; if the number of right continuous interruption units that have not exceeded the echo amplitude threshold is greater than the maximum interruption interval of the right continuous distance unit, the right traversal starting from DisResult[i][j] ends; when the number of left continuous interruption units that have not exceeded the echo amplitude threshold is less than the preset maximum interruption interval of the left continuous distance unit, the traversal continues; when both left and right traversals end, this aggregation block is recorded. If DisResult[i][j] is located at the top of the pitch layer at this time, then the current traversal of the agglomerate ends;

[0011] If the pitch level is between the first and last levels, then add one to the pitch level i in DisResult[i][j]. The distance cell number is the same as that in DisResult[i][j]. That is, start searching for convergence points by traversing to the left and right consecutive cells from DisResult[i+1][j], and store the successfully associated convergence points in the current cell. In the middle; when the point in the agglomerate After saving, The centroid is calculated using the pitch condensation formula to obtain the condensation result of the current pitch block. The pitch dimension condensation result of the current azimuth is then processed. When the pitch at the starting traversal point is the top layer, the pitch dimension condensation in this azimuth ends.

[0012] Optionally, the pitch condensation formula is: Where el represents the pitch angle, el i A represents the elevation of the echo signal of the i-th point. i The amplitude of the echo signal of the i-th point is represented, and n represents the number of processed points in the agglomerate.

[0013] Optionally, after the aggregation of each elevation unit in that azimuth is completed, starting from the current azimuth plane, the range and elevation dimensions are aggregated sequentially for the next azimuth, including:

[0014] Determine if there is a cohesion starting azimuth plane, i.e., determine if the corresponding range-dimensional cohesion and pitch-dimensional cohesion have been completed in the same azimuth. If so, define a pointer to the starting address of Vector[i][] pointing to the first pitch result and start traversing. Pop the azimuth, pitch, and range cells of the cohesion points in the current Vector[i][1] and search for the points currently associated with Vector[i][]. Use the azimuth cohesion criterion to search for cohesion blocks in consecutive preset number of pitch and preset number of azimuths. Determine if the azimuth of Vector[i][1] differs from the current azimuth by a preset number of azimuths. If the preset number is not reached, retain the Vector[i][] cohesion block and continue traversing the cohesion blocks associated with Vector[i+1][]. If the azimuth difference reaches the preset number, use the range, pitch, and azimuth cohesion formulas to calculate the points in the Vector[i][] cohesion block, clear the points in the Vector[i][] cohesion block, and calculate the centroid to obtain the azimuth, pitch, and range parameters after target cohesion.

[0015] Optionally, the calculation of the centroid to obtain the target's azimuth, pitch, and range parameters after convergence includes:

[0016] According to the azimuth-dimensional condensation formula The centroid value is used to determine the azimuth, pitch, and range parameters of the target after convergence, where Az represents the pitch angle. i A represents the elevation of the echo signal of the i-th point. i The amplitude of the echo signal of the i-th point is represented, and n represents the number of processed points in the agglomerate.

[0017] Optionally, the preset quantity is 3, and the preset number of layers is 3.

[0018] Optionally, the output aggregated dot information includes:

[0019] Multiple aggregate blocks after azimuth aggregation are processed. Each aggregate block contains azimuth, elevation, and distance information of multiple points. The aggregated point information is finally output by processing each aggregate block after calculating the centroid of the distance unit, azimuth unit, and elevation unit.

[0020] The beneficial effects of this invention are as follows:

[0021] This invention comprehensively considers the echo characteristics of split and weak targets. First, it sets corresponding thresholds when processing the raw echo data. Then, for data exceeding the thresholds, it uses the centroid method to perform convergence processing on the range, elevation, and azimuth units respectively. This achieves both avoiding the filtering out of weak targets and considering the splitting phenomenon of targets. In other words, this invention is based on the existing centroid method for azimuth determination, quantizing the echo data one by one according to the range, elevation, and azimuth units, then converging the range, elevation, and azimuth units sequentially, and then processing the raw echo data step by step, finally outputting accurate point parameters. Experimental results show that this algorithm can accurately distinguish neighboring targets at different azimuths or ranges, while avoiding the formation of multiple observation points for the same target at the range or azimuth.

[0022] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0023] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0024] Figure 1 This is a flowchart illustrating the search radar echo point aggregation processing method provided in an embodiment of the present invention.

[0025] Figure 2 A flowchart illustrating the distance-dimensional agglomeration algorithm implemented in an embodiment of the present invention;

[0026] Figure 3 A flowchart illustrating the implementation of the pitch-dimensional cohesive algorithm provided in an embodiment of the present invention;

[0027] Figure 4 This is a flowchart illustrating the implementation of the orientation-dimensional aggregation algorithm provided in an embodiment of the present invention. Detailed Implementation

[0028] The existing centroid method for azimuth convergence has several drawbacks. Firstly, it extracts incomplete target information, only considering target splitting in the azimuth and neglecting the effects of target elevation clutter and splitting. Secondly, it does not set a corresponding azimuth echo amplitude threshold when calculating the centroid, leading to large fluctuations in the calculation results and significant measurement errors. Thirdly, the existing beamforming method still ignores target splitting, resulting in relatively absolute target point extraction and significant extraction errors. Furthermore, this method removes a large amount of splitting information, easily overlooking some weak targets and providing a one-sided extraction of target parameters. In this invention, the echo data is quantized sequentially according to range, elevation, and azimuth units, then converged sequentially for each unit, and the original echo data is processed step by step to finally output accurate point parameters. The invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and do not limit the invention.

[0029] This invention provides a method for processing radar echo point clustering, see [link to relevant documentation]. Figure 1 The method includes:

[0030] S101. Based on the echo characteristics of split targets and weak targets, set an echo amplitude threshold value;

[0031] S102. For the original echo data that exceeds the echo amplitude threshold, first perform agglomeration on different range units of the original echo data at the same azimuth and the same elevation. When the range dimension agglomeration of each elevation unit at the same azimuth unit is completed, store the range dimension agglomeration result of each elevation unit and record it as the range dimension agglomeration result.

[0032] S103. Use the distance dimension aggregation result to aggregate each pitch dimension at a certain azimuth and store the pitch dimension aggregation result at the current azimuth.

[0033] S104. After the elevation dimension of each azimuth is condensed, starting from the current azimuth plane, the distance dimension and elevation dimension are condensed sequentially for the next azimuth. When the Nth azimuth is traversed, the condensed point information is output.

[0034] In other words, this embodiment of the invention comprehensively considers the echo characteristics of split targets and weak targets. First, it sets corresponding thresholds when processing the original echo data. Then, for data exceeding the thresholds, it uses the centroid method to perform convergence processing on the range, elevation, and azimuth units respectively. This achieves both avoiding the filtering out of weak targets and considering the splitting phenomenon of targets. Specifically, the method described in this embodiment of the invention is based on the existing centroid method for azimuth determination. The echo data is quantized one by one according to the range, elevation, and azimuth units, and then converged sequentially on the range, elevation, and azimuth units. The original echo data is then processed step by step, ultimately outputting accurate point parameters.

[0035] Specifically, the embodiments of the present invention utilize the centroid method to weighted aggregate the amplitudes of the echo signals from the target's nearest range unit, elevation unit, and azimuth unit, and design corresponding aggregation rules. This allows for the differentiation and processing of the target's true traces and traces caused by false alarms in the echo data, eliminating clutter data in the echoes. This greatly ensures the reliability of the search radar echo data and provides accurate trace parameters for the search radar's Track-While-Scan (TWS) operation.

[0036] The overall design concept of the method described in this embodiment of the invention is as follows: First, the original echo data is aggregated across different range units within the same azimuth and elevation units. After the range dimension aggregation of each elevation unit within the same azimuth unit is completed, the range dimension aggregation result of each elevation unit is stored and recorded as the range dimension aggregation result. Then, the range dimension aggregation result is used to aggregate each elevation unit in that azimuth, and the elevation dimension aggregation result for the current azimuth is stored. After the aggregation of each elevation unit in that azimuth is completed, starting from the current azimuth plane, the above range and elevation dimension aggregation is sequentially performed on the next azimuth. When the third azimuth is reached, the aggregated point information is output. The overall process framework of the method described in this invention is as follows: Figure 1 As shown.

[0037] The method described in this invention mainly includes the following steps:

[0038] Step 1: Determine the quantitative parameters in the algorithm

[0039] (1) Determine the signal processing echo amplitude threshold;

[0040] (2) Define the maximum range cell (NJMAXDIS), minimum range cell (NJMINDIS), maximum elevation layer (NJMAXELE), minimum elevation layer (NJMINELE), maximum azimuth layer (NJMAXAZ), and minimum azimuth layer (NJMINAZ) based on the characteristics of the search radar's transmit beam.

[0041] (3) Determine the maximum interruption interval threshold (DISINTERMAX) of the continuous range unit corresponding to range cohesion, determine the maximum interruption interval (ELELEFTINTERMAX) of the left range continuous unit and the maximum interruption interval (ELERIGHTINTERMAX) of the right range continuous unit during pitch cohesion, and determine the maximum interruption interval (AZILEFTINTERMAX) of the left range continuous unit and the maximum interruption interval (AZIRIGHTINTERMAX) of the right range continuous unit during azimuth cohesion.

[0042] Step 2: Determine the weighted centroid method formula for this algorithm.

[0043] In this invention, the distance, pitch, and azimuth dimensions are all calculated using the center-of-centimeter method, and the specific calculation formulas are as follows:

[0044] Distance-dimensional condensation formula: Pitch-dimensional condensation formula:

[0045] Azimuth dimensional condensation formula:

[0046] R represents distance, R i A represents the distance of the echo signal from the i-th point. i Let el represent the amplitude of the echo signal at the i-th point, and let el represent the pitch angle. i Let Az represent the pitch of the echo signal of the i-th point, and let Az represent the pitch angle. i The pitch of the echo signal of the i-th point is represented by , and n represents the number of processed points in the agglomerate.

[0047] The aggregation result of this agglomerate can be obtained by finding the centroid of the agglomerate.

[0048] Step 3: Distance Dimension Condensation

[0049] (1) Determine the starting distance unit, and use this distance unit as the starting point to find the points where the echo amplitude exceeds the threshold within the continuous distance units.

[0050] (2) Determine the distance-dimensional aggregation criterion: When the interruption interval during the indexing process is greater than DISINTERMAX, calculate the centroid of this aggregation block using the distance-dimensional aggregation formula, and record the result of the current aggregation block in the DisResult[nEle][distanctD] structure array. Wherein, nEle represents the current pitch cell number, and distanctD represents the distance centroid value of the current aggregation block calculated by distance-dimensional aggregation.

[0051] Step 4: Pitch Dimension Condensation

[0052] (1) When entering pitch dimension condensation, first determine whether the pitch unit nEle in the current distance dimension condensation result is equal to NJMAXELE. If nEle in the distance dimension condensation result does not satisfy the maximum pitch, pitch dimension condensation is not performed.

[0053] (2) Determine the pitch dimension aggregation criterion: Pitch dimension aggregation is based on the distance dimension aggregation result, and different aggregation rules are used according to the current pitch layer. When the pitch dimension aggregation condition is met, that is, when nEle is equal to NJMAXELE, the indexing starts from the first pitch layer upward. During the indexing process, when the pitch layer has not reached the top layer, the aggregation block is traversed in the same distance cell between two adjacent layers using a window-swiping method. That is, the aggregation block is continued to be searched in DisResult[i][j] and DisResult[i][j]. Here, i represents the pitch layer and j represents the current distance cell. The aggregation block traversal here is different from the distance dimension aggregation traversal. The index interruption interval here takes into account the front and back points of the current distance cell. The front and back points are called left and right continuous points in this invention. When the index interruption interval is greater than ELELEFTINTERMAX and greater than ELEIGHTINTERMAX, the distance and pitch centroid of this index block are calculated and the results are stored in the EleList result body array.

[0054] Step 5: Convergence of Orientation Dimensions

[0055] (1) After the pitch dimension condensation is completed, the algorithm enters the azimuth dimension condensation process, which depends on the results of the pitch dimension condensation. First, the starting azimuth condensation is determined. The starting azimuth condensation must satisfy the following: the pitch dimension condensation at this azimuth should be completed. At the same time, the pitch dimension condensation results are put into the two-dimensional structure array PointGroupList in sequence to ensure that the PointGroupList structure array is not empty.

[0056] (2) Determine the azimuth aggregation criteria: To ensure the validity of the aggregated point data, the starting traversal point needs to be established during azimuth aggregation. When the starting traversal point is located in the first layer of elevation, the azimuth aggregation block is composed of three consecutive layers in the azimuth direction to the right and in the elevation direction. When the starting traversal point is located in the last layer of elevation, the azimuth aggregation block is composed of three consecutive layers in the azimuth direction to the right and in the elevation direction to the bottom that meet the conditions. When the starting traversal point is between the first and last layers, the azimuth aggregation block is composed of three consecutive layers in the azimuth direction to the right and in the elevation direction, with the layer as the center, extending one layer upward and downward respectively. The traversal method of adjacent distance units on each elevation layer is consistent with that of elevation dimension aggregation. When traversing the point, when the point found meets the preset amplitude conditions, the point information is stored in the point block, and the associated point is cleared from the elevation dimension aggregation result to avoid duplicate aggregation.

[0057] Step 6: Output the condensed dots

[0058] After azimuth aggregation, multiple aggregation blocks will be formed, and each point block contains the azimuth, elevation, and distance information of multiple points. By calculating the centroid of the distance unit, the centroid of the azimuth unit, and the centroid of the elevation unit for each point block, the aggregated point can be output.

[0059] The following will combine Figures 1 to 4 The method described in this embodiment of the invention will be illustrated by a specific example:

[0060] like Figure 1 As shown, the method described in this embodiment of the invention first obtains the original echo data, then performs range-dimensional aggregation, elevation-dimensional aggregation, and finally azimuth-dimensional aggregation on the original echo data. Finally, the centroid of the aggregation block in the azimuth dimension is calculated to obtain the azimuth, elevation, and range information of the aggregated point.

[0061] See Figure 2As shown, when starting range-dimensional aggregation, range aggregation is first performed at the same azimuth and elevation. During aggregation, it is first necessary to determine whether the echo value of the current range cell has exceeded a threshold. If it does not meet the threshold requirement, the current range cell number is incremented by 1, and the process of determining whether the echo cell has exceeded the threshold condition continues. If it meets the threshold requirement, the process iterates through the range cells that have continuously exceeded the threshold. During the traversal, the number of consecutive interrupted cells that have not exceeded the threshold is determined. If the number of interrupted cells is greater than a preset value, the traversal of this aggregation block is terminated, the centroid of the range aggregation block traversed in this traversal is calculated to obtain the aggregation result of this block, and then the starting number of the range cells traversed in the next traversal is updated, and a new aggregation block is started. If the number of consecutive interrupted cells that have not exceeded the threshold is less than the preset value, the traversal is incremented by 1 based on the current traversed range cells, and the traversal continues, without restarting the aggregation block. If the current aggregation cell is greater than the maximum aggregation range cell, the range cell aggregation of the current elevation layer ends, and the aggregation results of all aggregation blocks are saved in DisResult[nEle][distanctD].

[0062] See Figure 3 When starting pitch-dimensional convergence, it is first necessary to determine whether the current pitch echo is in the last layer. If it is not in the last layer, pitch-dimensional convergence is not performed. When the pitch echo is in the last layer, pitch-dimensional convergence begins. At this time, the distance-dimensional convergence result of the first layer pitch is used to start traversing to find the pitch convergence block. During the traversal, starting from the traversal point DisResult[i][j], traversing to the left and right continuous distance cells respectively. If the number of left continuous interruptions that have not exceeded the threshold is greater than the preset value, the left traversal starting from DisResult[i][j] ends; if the number of right continuous interruptions that have not exceeded the threshold is greater than the preset value, the right traversal starting from DisResult[i][j] ends. When both left and right traversals are finished, this convergence block is recorded. If DisResult[i][j] is at the top pitch layer, the current agglomeration traversal ends. If the pitch layer is between the first and last layers, the pitch layer number i in DisResult[i][j] needs to be increased by one, and the distance cell number is the same as that in DisResult[i][j]. That is, starting from DisResult[i+1][j], we traverse to the left and right consecutive cells to find agglomeration points, and store the successfully associated agglomeration points. In the middle. When the point in the agglomerate. After saving, The centroid can be calculated using the pitch condensation formula to obtain the condensation result of the current pitch block, and this result is stored in EleList. When the pitch at the starting traversal point is the top layer, the pitch dimension condensation in this azimuth ends.

[0063] See Figure 4When entering azimuth convergence, it is first necessary to determine whether there is a azimuth plane for convergence, that is, to determine whether the corresponding range-dimensional convergence and pitch-dimensional convergence have been completed in the same azimuth, and to store the results in a two-dimensional Vector structure (PointGroupList). If the PointGroupList structure is empty, then all pitch-dimensional convergence results in the current azimuth need to be stored in a two-dimensional Vector, and each pitch-dimensional result is located in the first inner value of the outer Vector, that is, Vector[i][1]. If PointGroupList is not empty, define a pointer to the address of Vector[i][] of the first pitch result and start traversing. Pop the azimuth, pitch, and distance cells of the current convergent points in Vector[i][1] and search for the current points that can be associated with Vector[i][]. Use the azimuth convergence criterion to search for convergent blocks in three consecutive pitch and three azimuth layers. Determine if the azimuth of Vector[i][1] differs from the current azimuth by 3 azimuths. If it does not reach three azimuths, retain the convergent block of Vector[i][] and continue traversing the convergent blocks associated with Vector[i+1][]. If the azimuth difference is 3, use the distance, pitch, and azimuth convergence formulas to calculate the points in the convergent block of Vector[i][] and clear the points in the convergent block of Vector[i][]. The centroid value calculated by the formula is the azimuth, pitch, and distance parameters after target convergence.

[0064] In summary, the centroid-based point preprocessing algorithm described in this invention can significantly reduce point splitting in range, azimuth, and pitch, providing a reliable point foundation for subsequent track processing. Furthermore, the method described in this embodiment can be applied to search radar data processing software, and multi-target search experiments have been conducted. Experimental results show that this algorithm can accurately distinguish neighboring targets at different azimuths or ranges, while avoiding the formation of multiple observation points for the same target at different ranges or azimuths.

[0065] Although preferred embodiments of the invention have been disclosed for illustrative purposes, those skilled in the art will recognize that various modifications, additions, and substitutions are possible, and therefore the scope of the invention should not be limited to the embodiments described above.

Claims

1. A method for processing concentrated radar echo points, characterized in that, include: Based on the echo characteristics of split targets and weak targets, an echo amplitude threshold is set; For raw echo data exceeding the aforementioned echo amplitude threshold, firstly, the raw echo data is aggregated into different range units at the same azimuth and elevation. Once the range dimension aggregation of each elevation unit at the same azimuth is completed, the range dimension aggregation result of each elevation is stored and recorded as the range dimension aggregation result. Then, the range dimension aggregation result is used to aggregate each elevation dimension at a certain azimuth, and the elevation dimension aggregation result at the current azimuth is stored. Once the aggregation of each elevation dimension at that azimuth is completed, starting from the current azimuth plane, the range dimension and elevation dimension aggregation are sequentially performed on the next azimuth. When the Nth azimuth is reached, the aggregated point information is output. The process of using distance-dimensional convergence results to converge various pitch dimensions at a certain azimuth and storing the pitch-dimensional convergence results at the current azimuth includes: Determine if the current pitch echo is in the last layer. If so, use the range dimension aggregation result of the first layer pitch to start traversing to find the pitch aggregation block. During the traversal, start from the traversal point DisResult[i][j] and traverse to the left and right continuous range units respectively, where i is the pitch layer number and j is the current range unit. If the number of left continuous interruption units that have not exceeded the echo amplitude threshold is greater than the preset maximum interruption interval of the left range continuous unit, the left traversal starting from DisResult[i][j] ends. If the number of right continuous interruption units that have not exceeded the echo amplitude threshold is greater than the maximum interruption interval of the right range continuous unit, the right traversal starting from DisResult[i][j] ends. When the number of left continuous interruption units that have not exceeded the echo amplitude threshold is less than the preset maximum interruption interval of the left range continuous unit, the traversal continues. When both left and right traversals are completed, record this aggregation block. If DisResult[i][j] is located at the top of the pitch layer at this time, then the current traversal of the agglomerate ends; If the pitch level is between the first and last levels, then add one to the pitch level i in DisResult[i][j]. The distance cell number is the same as that in DisResult[i][j]. That is, start searching for convergence points by traversing to the left and right consecutive cells from DisResult[i+1][j], and store the successfully associated convergence points in the current cell. In the middle; when the point in the agglomerate After saving, The centroid is calculated using the pitch condensation formula to obtain the condensation result of the current pitch block, and the pitch dimension condensation result of the current azimuth is stored. When the pitch of the starting traversal point is the top layer, the pitch dimension condensation of this azimuth ends.

2. The method according to claim 1, characterized in that, The method further includes: Based on the characteristics of the transmitted beam of the searched radar, set the maximum concentration range cell, the minimum concentration range cell, the maximum number of concentration elevation layers, the minimum number of elevation concentration layers, the maximum number of concentration azimuth layers, and the minimum number of concentration azimuth layers. Simultaneously, the maximum interruption interval threshold of the continuous distance unit corresponding to distance cohesion, the maximum interruption interval of the left and right continuous distance units during pitch cohesion, and the maximum interruption interval of the left and right continuous distance units during azimuth cohesion were determined.

3. The method according to claim 1, characterized in that, The agglomeration of different distance cells in the same azimuth and elevation dimensions of the original echo data includes: Traverse the distance cells that have continuously exceeded the threshold. During the traversal, determine the number of consecutive interrupted cells that have not exceeded the echo amplitude threshold. If the number of consecutive interrupted cells is greater than the preset maximum interruption interval threshold, the traversal of this agglomerate block is terminated. Calculate the centroid of the distance agglomerate block in this traversal to obtain the agglomeration result of this block. Then update the starting number of the distance cells for the next traversal and start a new agglomerate block to continue agglomeration. Otherwise, increment the number of distance cells in the current traversal by 1 and continue traversing. If the current number of agglomerate cells is greater than the preset maximum agglomeration distance cell, the agglomeration of the distance cells in the current pitch layer ends, and the agglomeration results of the distance dimension of all agglomerates are saved.

4. The method according to claim 3, characterized in that, The process of determining the centroid of the traversed agglomerates to obtain the agglomeration result of the block includes: According to the distance condensation formula The centroid of the distance-bound condensate block is calculated to obtain the condensation result of this block; where R represents the distance, R i A represents the distance of the echo signal from the i-th point. i The amplitude of the echo signal of the i-th point is represented, and n represents the number of processed points in the agglomerate.

5. The method according to claim 1, characterized in that, The pitch-cohesion formula is: Where el represents the pitch angle, el i A represents the elevation of the echo signal of the i-th point. i The amplitude of the echo signal of the i-th point is represented, and n represents the number of processed points in the agglomerate.

6. The method according to claim 1, characterized in that, Once the elevation units in that azimuth are condensed, starting from the current azimuth plane, the range and elevation dimensions are condensed sequentially for the next azimuth, including: Determine if there is a cohesion starting azimuth plane, i.e., determine if the corresponding range-dimensional cohesion and pitch-dimensional cohesion have been completed in the same azimuth. If so, define a pointer to the starting address of Vector[i][] pointing to the first pitch result and start traversing. Pop the azimuth, pitch, and range cells of the cohesion points in the current Vector[i][1] and search for the points currently associated with Vector[i][]. Use the azimuth cohesion criterion to search for cohesion blocks in consecutive preset number of pitch and preset number of azimuths. Determine if the azimuth of Vector[i][1] differs from the current azimuth by a preset number of azimuths. If the preset number is not reached, retain the Vector[i][] cohesion block and continue traversing the cohesion blocks associated with Vector[i+1][]. If the azimuth difference reaches the preset number, use the range, pitch, and azimuth cohesion formulas to calculate the points in the Vector[i][] cohesion block, clear the points in the Vector[i][] cohesion block, and calculate the centroid to obtain the azimuth, pitch, and range parameters after target cohesion.

7. The method according to claim 6, characterized in that, The calculation of the centroid yields the target's azimuth, pitch, and range parameters after convergence, including: According to the azimuth-dimensional condensation formula The centroid value is used to determine the azimuth, pitch, and range parameters of the target after convergence, where Az represents the pitch angle. i A represents the elevation of the echo signal of the i-th point. i The amplitude of the echo signal of the i-th point is represented, and n represents the number of processed points in the agglomerate.

8. The method according to claim 6, characterized in that, The preset quantity is 3, and the preset number of layers is 3.

9. The method according to any one of claims 1-8, characterized in that, The output aggregated dot information includes: Multiple aggregate blocks after azimuth aggregation are processed. Each aggregate block contains azimuth, elevation, and distance information of multiple points. The aggregated point information is finally output by processing each aggregate block after calculating the centroid of the distance unit, azimuth unit, and elevation unit.

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