Binary accumulation target detection method of self-adaptive sliding window

Abstract

The invention discloses a binary accumulation target detection method of an adaptive sliding window. The method comprises the steps that pulse pressure data of a radar in a scanning period are obtained, the pulse pressure data are obtained by conducting pulse pressure processing on echo data obtained by mechanically scanning a radar scanning area, and the scanning area comprises a target; based on the pulse pressure data in one scanning period, a two-dimensional matrix is obtained, the two-dimensional matrix comprises a plurality of distinguishing units, and each distinguishing unit comprises amplitude information; processing the two-dimensional matrix based on a first-level threshold to obtain a 0 / 1 table; and obtaining an adaptive sliding window stepping and window criterion set for the 0 / 1 table. According to the invention, the technical problems of a large amount of redundant calculation, low detection efficiency and large information loss of a sliding window type binary accumulation detection method in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of radar target detection, and in particular to a binary accumulation target detection method of an adaptive sliding window. Background Art

[0002] For mechanical scanning radar, when the antenna is continuously scanned, a series of echo pulses will be obtained. The traditional binary accumulation detection method performs threshold detection on each pulse data, and then stores the obtained "0" or "1", and then optimally accumulates the "0" or "1" of M pulses in the same distance unit to achieve target detection; the commonly used detection means is the sliding window detector, but the traditional sliding window detector has many redundant calculations and practical problems. This method improves it. First, it reduces unnecessary redundant calculations and improves efficiency; second, it retains the amplitude distribution of the target as much as possible to provide more detailed information for subsequent condensation work. Summary of the invention

[0003] The embodiment of the present invention provides an adaptive sliding window binary accumulation target detection method to at least solve the technical problems of a large amount of redundant calculation, low detection efficiency and large information loss in the sliding window binary accumulation detection method in the prior art.

[0004] According to one aspect of an embodiment of the present invention, a binary accumulation target detection method with an adaptive sliding window is provided. The method may include: obtaining pulse pressure data within a scanning cycle of the radar, wherein the pulse pressure data is obtained by pulse pressure processing of echo data obtained from a scanning area of ​​a mechanical scanning radar, wherein the scanning area contains a target; obtaining a two-dimensional matrix based on the pulse pressure data within a scanning cycle, wherein the two-dimensional matrix contains a number of resolution units, and each resolution unit includes amplitude information; processing the two-dimensional matrix based on a first-level threshold to obtain a 0 / 1 table; obtaining an adaptive sliding window step and window criteria set for the 0 / 1 table; performing sliding window processing on the 0 / 1 table based on a sliding window with an adaptive sliding window step, when the sliding When the window slides to a position where the quotient of the number of 1s in the 0 / 1 table and the sliding window length is greater than or equal to the window criterion, the window contains detection point information, wherein the output of the sliding window is the position information and amplitude information of the central resolution unit of the 0 / 1 table corresponding to the sliding window; the central resolution unit position of the window containing the detection point information is converted into the distance and azimuth in the actual scanning area; the distance, azimuth and amplitude of all detection points in the entire scanning area constitute the original point trace data; the original point trace data is condensed to generate condensed points, and the condensed points are used as the results of target detection in the scanning area.

[0005] Optionally, obtaining a two-dimensional matrix based on the pulse compression data within one scanning period includes: quantizing the pulse compression data within one scanning period in the azimuth dimension and the range dimension according to azimuth units and range units to generate a two-dimensional matrix.

[0006] Optionally, processing the two-dimensional matrix based on a first-level threshold to obtain a 0 / 1 table includes: when the amplitude in a resolution cell in the two-dimensional matrix is greater than or equal to the first-level threshold, setting the corresponding resolution cell to 1; when the amplitude in a resolution cell in the two-dimensional matrix is less than the first-level threshold, setting the corresponding resolution cell to 0.

[0007] Optionally, performing window hopping sliding on the 0 / 1 table. If the number of 1s in the 0 / 1 table corresponding to the sliding window is equal to 0, the number of 1s in the current window is 0. If the number of 1s in the 0 / 1 table corresponding to the sliding window is non-zero, the number of 1s in the current window is non-zero, and the current cumulative number of 1s is the sum of the previous cumulative number of 1s and the number of 1s in the current window. Among them, the window hopping step of the window hopping sliding is the window length, and the sliding window criterion is M / N, where M is the minimum number of 1s that should be contained when there is a target in the window, and N is the window length of the sliding window. After each window sliding, if the current cumulative number of 1s is less than the minimum number of 1s that should be contained when there is a target in the window, continue to search using window hopping sliding. If the current cumulative number of 1s is greater than or equal to the minimum number of 1s that should be contained when there is a target in the window, return one window hopping for refined search. During the refined search process, if the number of 1s in the 0 / 1 table corresponding to the window is 1, the window hopping step of the window is the difference between the minimum number of 1s that should be contained when there is a target in the window and the number of 1s in the 0 / 1 table. If the number of 1s in the 0 / 1 table corresponding to the window is 2, the window hopping step of the window is the difference between the minimum number of 1s that should be contained when there is a target in the window and the number of 1s in the 0 / 1 table. If the number of 1s in the 0 / 1 table corresponding to the window is 3, the window hopping step of the window is 1. If the number of 1s in the 0 / 1 table corresponding to the window is 4, the window hopping step of the window is 1. Repeat the above steps to obtain the expression for the adaptive window hopping step as: S = max(M - m, 1) Where S is the adaptive sliding window step, m is the number of 1s in the 0 / 1 table corresponding to the sliding window, and max is the maximum value function.

[0008] Optionally, the process of determining the amplitude of each window detection point is: determining the amplitude of each window detection point based on the amplitude at the corresponding position of the resolution cell of each window.

[0009] Optionally, determining the amplitude of each window detection point based on the amplitude at the corresponding position of each window resolution unit includes: when the amplitude at the corresponding position of the central resolution unit of each window is non-zero, using the amplitude at the corresponding position of the central resolution unit of each window as the amplitude of the detection point; when the amplitude at the corresponding position of the central resolution unit of each window is 0, determining the maximum value of the amplitudes at the corresponding positions of the resolution units adjacent to both sides of the central resolution unit of each window as the amplitude of the detection point; when the amplitudes at the corresponding positions of the resolution units adjacent to both sides of the central resolution unit of each window are 0, determining the maximum value of the amplitudes at the corresponding positions of the resolution units adjacent to both sides of the adjacent resolution units as the amplitude of the detection point.

[0010] Advantages of the present invention: The improved adaptive sliding window binary accumulation detection method designed by the present invention, compared with the traditional method, adopts the basic idea of "large-step search and small-step refinement" for the specific application of mechanical scanning radar, analyzes various situations in the sliding window processing in detail, deletes the redundant calculation part, and is more concise and efficient; and for the problem of detection point selection, the corresponding optimal position method is proposed, which completely retains the amplitude distribution characteristics of the pulse echo, provides sufficient prior information for subsequent aggregation and detection processing, and provides information support for the improvement of subsequent processing accuracy. Description of the drawings

[0011] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 is a flowchart of an adaptive sliding window binary accumulation target detection method according to an embodiment of the present invention; Figure 2 is a schematic diagram of binary accumulation detection of the same range unit according to an embodiment of the present invention; Figure 3 is a schematic diagram of the "flat top effect" of the maximum amplitude method according to an embodiment of the present invention; Figure 4 is a schematic diagram of the corresponding optimal position method designed according to an embodiment of the present invention. Detailed implementation manners

[0012] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0013] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects and are used to describe a specific order or sequence. It should be understood that such terms can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0014] Embodiment 1 According to an embodiment of the present invention, there is provided a binary accumulation target detection method with an adaptive sliding window. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system including at least a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0015] Figure 1 is a flowchart of a binary accumulation target detection method with an adaptive sliding window according to an embodiment of the present invention, as Figure 1 shown, the method may include the following steps: Step S101, obtain the pulse compression data within one scanning period of the radar. Among them, the pulse compression data is obtained by pulse compression processing of the echo data acquired from the scanning area of the mechanical scanning radar, and the scanning area contains targets.

[0016] In the technical solution provided in step S101 of the present invention above, obtain the pulse compression data within one scanning period of the radar.

[0017] Step S102, based on the pulse compression data within one scanning period, obtain a two-dimensional matrix. Among them, the two-dimensional matrix contains a number of resolution cells, and each resolution cell includes amplitude information.

[0018] In the technical solution provided in step S102 of the present invention above, process the pulse compression data within one scanning period to obtain a two-dimensional matrix.

[0019] Step S103, process the two-dimensional matrix based on a first-level threshold to obtain a 0 / 1 table.

[0020] In the technical solution provided in step S103 of the present invention above, process the two-dimensional matrix using a first-level threshold to obtain a 0 / 1 table.

[0021] Step S104: Obtain the adaptive sliding window step and window criterion set for the 0 / 1 table.

[0022] In the technical solution provided in step S104 of the present invention, the sliding window method can be divided into three types. One is sliding window by one, where the sliding window step S is 1; the second is skipping window sliding, where the sliding window step S is the window length N; the third is stepped sliding window, where the sliding window step is S (1 < S < N).

[0023] For the traditional sliding window method, the sliding window step is a fixed value. To ensure that each pulse is not missed in processing, the first method of sliding window by one needs to be adopted. This method improves the traditional fixed-step sliding window method by combining the three sliding window methods and removing the redundant calculation part.

[0024] Figure 2 It is a schematic diagram of binary accumulation detection for the same range cell according to an embodiment of the present invention, as Figure 2 shown. For example, set the sliding window criterion M / N = 3 / 5, that is, if there are at least 3 "1"s among 5 pulses in the same range cell, it is determined that there is a target at the positions of these 5 pulses.

[0025] During the processing, for sliding window 1 (1# - 5#), the number m of 1s in the 0 / 1 table corresponding to sliding window 1 is 0, which is less than 3. When detecting sliding window 2 (2# - 6#) and sliding window 3 (3# - 7#), regardless of whether 6# and 7# are "0" or "1", m will not exceed 3 at most. Therefore, the calculations of sliding window 2 and sliding window 3 are redundant calculations. If the resolution units 6# - 10# are "0", then the calculations of sliding window 4, sliding window 5, and sliding window 6 are all redundant calculations, where m is the number of 1s in the 0 / 1 table corresponding to any sliding window.

[0026] First, adopt the large-step search method, that is, skip window, with the sliding window step S = N. If m = 0, then the cumulative target number sum = 0; if m ≠ 0, then the cumulative target number sum = sum + m; after each sliding window, make a determination. If sum < M, continue to skip window for large-step search. If sum ≥ M, backtrack one skip window for small-step refinement search.

[0027] Step S105: Perform sliding window processing on the 0 / 1 table with an adaptive sliding window step based on the sliding window. When the sliding window slides to a position where the quotient of the number of 1s in the 0 / 1 table and the window length of the sliding window is greater than or equal to the window criterion, then this window contains detection point information. Among them, the output of this sliding window is the position information and amplitude information of the center resolution unit of the 0 / 1 table corresponding to the sliding window.

[0028] In the technical solution provided in step S105 of the present invention, the 0 / 1 table is processed by a sliding window with an adaptive sliding window step. If m / N is greater than or equal to M / N, it is determined that there is a target in the window. The output of the sliding window is the position information and amplitude information of the central resolution unit of the 0 / 1 table corresponding to the sliding window.

[0029] Step S106: Convert the position of the central resolution unit of the window containing detection point information into the distance and azimuth in the actual scanning area.

[0030] In the technical solution provided in step S106 of the present invention, the position of the central resolution unit of the window containing detection point information is converted into the distance and azimuth in the actual scanning area.

[0031] Step S107: The distance, azimuth, and amplitude of all detection points in the entire scanning area constitute the original point trace data.

[0032] In the technical solution provided in step S107 of the present invention, the distance, azimuth, and amplitude of all detection points in the entire scanning area constitute the original point trace data.

[0033] Step S108: Perform clustering processing on the original point trace data to generate clustered points, and use the clustered points as the result of target detection in the scanning area.

[0034] In the technical solution provided in step S108 of the present invention, perform clustering processing on the original point trace data to generate clustered points, and use the clustered points as the result of target detection in the scanning area.

[0035] The above method of this embodiment will be further introduced below.

[0036] As an optional embodiment, step S102: Obtaining a two-dimensional matrix based on the pulse compression data within one scanning period includes: Quantizing the pulse compression data within one scanning period in the azimuth dimension and distance dimension according to azimuth units and distance units to generate a two-dimensional matrix.

[0037] In this embodiment, according to the pulse compression data within one scanning period, quantize in the azimuth dimension and distance dimension according to azimuth units and distance units to obtain a two-dimensional matrix.

[0038] As an optional embodiment, step S103: Processing the two-dimensional matrix based on a first-level threshold to obtain a 0 / 1 table includes: When the amplitude in the resolution unit of the two-dimensional matrix is greater than or equal to the first-level threshold, set the amplitude in the resolution unit to 1; When the amplitude in the resolution unit of the two-dimensional matrix is less than the first-level threshold, set the amplitude in the resolution unit to 0.

[0039] In this embodiment, if the amplitude in the resolution unit of the two-dimensional matrix is greater than or equal to the first-level threshold, the amplitude in the resolution unit is set to 1; when the amplitude in the resolution unit of the two-dimensional matrix is less than the first-level threshold, the amplitude in the resolution unit is set to 0; among them, the amplitude of each resolution unit not compared with the first-level threshold is saved.

[0040] As an alternative embodiment, in step S104, the determination process of the adaptive sliding window step set for the 0 / 1 table is as follows: The 0 / 1 table is slid with a skipped window. If the number of 1s in the 0 / 1 table corresponding to the sliding window is equal to 0, the number of 1s in the current window is 0. If the number of 1s in the 0 / 1 table corresponding to the sliding window is non-zero, the number of 1s in the current window is non-zero, and the current cumulative number of 1s is the sum of the previous cumulative number of 1s and the number of 1s in the current window; among them, the sliding step of the skipped window sliding is the window length, and the sliding window criterion is M / N, where M is the minimum number of 1s that should be contained when there is a target in the window, and N is the window length of the sliding window; after each window slide, if the current cumulative number of 1s is less than the minimum number of 1s that should be contained when there is a target in the window, continue to search with skipped window sliding; if the current cumulative number of 1s is greater than or equal to the minimum number of 1s that should be contained when there is a target in the window, retreat one skipped window for refined search; during the refined search process, if the number of 1s in the 0 / 1 table corresponding to the window is 1, the sliding step of the window is the difference between the minimum number of 1s that should be contained when there is a target in the window and the number of 1s in the 0 / 1 table; if the number of 1s in the 0 / 1 table corresponding to the window is 2, the sliding step of the window is the difference between the minimum number of 1s that should be contained when there is a target in the window and the number of 1s in the 0 / 1 table; if the number of 1s in the 0 / 1 table corresponding to the window is 3, the sliding step of the window is 1; if the number of 1s in the 0 / 1 table corresponding to the window is 4, the sliding step of the window is 1; repeating the above steps, the expression for the adaptive sliding window step is: S = max(M - m, 1) where S is the adaptive sliding window step, m is the number of 1s in the 0 / 1 table corresponding to the sliding window, and max is the maximum value function.

[0041] In this embodiment, first, a large-step search method is adopted, that is, skipped window sliding, and the sliding step S = N. If m = 0, the cumulative number of targets sum = 0; if m ≠ 0, the cumulative number of targets sum = sum + m; after each window slide, a determination is made. If sum < M setting value, continue with skipped window sliding for large-step search. If sum ≥ M, retreat one skipped window for small-step refined search, where m is the number of 1s in the O / 1 table corresponding to the window slide; The small-step refined search step should be S = max(M - m, 1), and the analysis is as follows: If m = 1 in the sliding window 1, at least two more resolution unit data need to be added to possibly achieve , the sliding window step S = 2, that is, M - m; If m = 2 in sliding window 1, then at least one more resolution cell data needs to be added to possibly achieve , the sliding window step S = 1, that is, M - m; If m = 3 in sliding window 1, then there is no need to add more resolution cell data and it has been achieved , the sliding window step S = 1, at this time M - m = 0 < 1; If m = 4 in sliding window 1, then there is no need to add more resolution cell data and it has been achieved , the sliding window step S = 1, at this time M - m = -1 < ... 1; By analogy, it can be found that the step should be S = max(M - m, 1).

[0042] As an optional embodiment, in step S105, the process of determining the amplitude of each window detection point is: based on the amplitudes at the corresponding positions of the resolution cells of each window, determine the amplitudes of each window detection point.

[0043] In this embodiment, according to the amplitudes at the corresponding positions of the resolution cells of each window, determine the amplitudes of each window detection point, where the amplitude is the amplitude of each resolution cell that has not been compared with the first-level threshold.

[0044] After the sliding window is determined and the M / N criterion is satisfied within the window, it becomes crucial which point's amplitude is used as the final detection point information. The common method is the maximum amplitude method, that is, selecting the maximum amplitude within the window as the amplitude at the center position of the detection window. However, the maximum amplitude method is very likely to cause the "flat top effect", resulting in a large subsequent aggregation azimuth error. Figure 3 is a schematic diagram of the "flat top effect" of the maximum amplitude method according to an embodiment of the present invention, as Figure 3 shown, when the length of the detection window is longer, the aggregation azimuth error will be larger; in this regard, the amplitude information at the corresponding position should be selected as the amplitude information of the final detection point of the detection window, which can more completely retain the amplitude distribution information of the echo pulse train of the same range cell.

[0045] Figure 4 is a schematic diagram of the corresponding optimal position method designed according to an embodiment of the present invention, but when Figure 4 there is a situation in the schematic diagram, the corresponding position method will have a "null effect", resulting in problems in the subsequent accumulation detection method. Therefore, the corresponding optimal position method is adopted, that is, first select the amplitude at the corresponding position. If the amplitude at the corresponding position is 0, then find the amplitude of the resolution cell with the largest amplitude that is not 0 and is closest to this resolution cell as the amplitude information of this resolution cell. The processing result is as Figure 4 shown in the corresponding optimal position method.

[0046] As an alternative embodiment, determining the amplitude of each window detection point based on the amplitude at the corresponding position of each window resolution unit includes: when the amplitude at the corresponding position of the central resolution unit of each window is non-zero, using the amplitude at the corresponding position of the central resolution unit of each window as the amplitude of the detection point; when the amplitude at the corresponding position of the central resolution unit of each window is 0, determining the maximum value of the amplitudes at the corresponding positions of the two adjacent resolution units adjacent to the corresponding position of the central resolution unit of each window as the amplitude of the detection point; when the amplitudes at the corresponding positions of the two adjacent resolution units adjacent to the corresponding position of the central resolution unit of each window are 0, determining the maximum value of the amplitudes at the corresponding positions of the two adjacent resolution units of the two adjacent resolution units as the amplitude of the detection point.

[0047] In this embodiment, for example, the position of the central resolution unit of a sliding window is (3, 4). When the amplitude at (3, 4) is 72, the amplitude of the detection point corresponding to this sliding window is 72. When the position of the central resolution unit of a sliding window is (3, 4) and the amplitude at (3, 4) is 0, that is, the overall position of this sliding window is (3, 2), (3, 3), (3, 4), (3, 5), (3, 6), determine the magnitude of the amplitudes at (3, 3) and (3, 5). When the amplitudes at (3, 3) and (3, 5) are non-zero, take the maximum value of the amplitudes at (3, 3) and (3, 5) as the amplitude of the detection point. When the amplitudes at (3, 3) and (3, 5) are 0, determine the magnitude of the amplitudes at (3, 2) and (3, 6). When the amplitudes at (3, 2) and (3, 6) are non-zero, take the maximum value of the amplitudes at (3, 2) and (3, 6) as the amplitude of the detection point.

[0048] In an embodiment of the present invention, by obtaining pulse compression data within one scanning period of a radar, where the pulse compression data is obtained by performing pulse compression processing on echo data acquired from the scanning area of a mechanical scanning radar, and the scanning area contains a target; based on the pulse compression data within one scanning period, a two-dimensional matrix is obtained, where the two-dimensional matrix includes a number of resolution cells, and each resolution cell includes amplitude information; the two-dimensional matrix is processed based on a first-level threshold to obtain a 0 / 1 table; an adaptive sliding window step size and window criterion set for the 0 / 1 table are obtained; the 0 / 1 table is subjected to sliding window processing based on a sliding window with the adaptive sliding window step size, and when the sliding window slides to a position where the quotient of the number of 1s in the 0 / 1 table and the window length of the sliding window is greater than or equal to the window criterion, the window contains detection point information, where the output of the sliding window is the position information and amplitude information of the central resolution cell of the 0 / 1 table corresponding to the sliding window; the position of the central resolution cell of the window containing detection point information is converted into the distance and azimuth in the actual scanning area; the distance, azimuth, and amplitude of all detection points in the entire scanning area constitute the original point track data; the original point track data is subjected to clustering processing to generate cluster points, and the cluster points are used as the result of target detection in the scanning area, solving the technical problems of a large amount of redundant calculation, low detection efficiency, and large information loss existing in the sliding window binary accumulation detection method in the prior art, achieving the technical effect of reducing redundant calculation, improving the detection efficiency, making less information lost, and restoring the amplitude distribution of the target echo as much as possible through an improved binary accumulation detection method with an adaptable sliding window step size designed, providing data support for the accuracy of subsequent clustering.

[0049] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0050] In the above embodiments of the present invention, the descriptions of the various embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0051] In several embodiments provided in the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of units can be a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces, and the indirect coupling or communication connection of units or modules can be in an electrical or other form.

[0052] The unit described as a separating component may or may not be physically separated, and the component shown as a unit may or may not be a physical unit, that is, it may be located in one place or may be distributed over multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0053] In addition, each functional unit in various embodiments of the present invention can be integrated into a first processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0054] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. An adaptive sliding window binary accumulation target detection method, characterized in that: include: Acquire pulse pressure data within a scanning cycle of the radar, wherein the pulse pressure data is obtained by pulse pressure processing of echo data acquired in a scanning area of ​​the mechanical scanning radar, wherein the scanning area contains a target; Based on the pulse pressure data in a scanning cycle, a two-dimensional matrix is ​​obtained, wherein the two-dimensional matrix includes a plurality of resolution units, and each resolution unit includes amplitude information; The two-dimensional matrix is ​​processed based on the first-level threshold to obtain a 0 / 1 table; Get the adaptive sliding window step and window criteria set for the 0 / 1 table; Based on the sliding window, the 0 / 1 table is processed with an adaptive sliding window step. When the sliding window slides to a position where the quotient of the number of 1s in the 0 / 1 table and the window length of the sliding window is greater than or equal to the window criterion, the window contains the detection point information, wherein the output of the sliding window is the position information and amplitude information of the central resolution unit of the 0 / 1 table corresponding to the sliding window; Convert the central resolution unit position of the window containing the detection point information into the distance and orientation in the actual scanning area; The distance, orientation and amplitude of all detection points in the entire scanning area constitute the original point trace data; The original point trace data is condensed to generate condensed points, which are used as the results of target detection in the scanning area.

2. The method according to claim 1, characterized in that The two-dimensional matrix is ​​obtained based on the pulse pressure data within a scanning cycle, including: Based on the pulse pressure data in one scanning cycle, the data is quantified in azimuth dimension and distance dimension according to azimuth unit and distance unit to generate a two-dimensional matrix.

3. The method according to claim 2, characterized in that The two-dimensional matrix is ​​processed based on the first-level threshold to obtain a 0 / 1 table, including: When the amplitude in the resolution unit in the two-dimensional matrix is ​​greater than or equal to the first-level threshold, the corresponding resolution unit is set to 1; When the amplitude in a resolution unit in the two-dimensional matrix is ​​less than the first-level threshold, the corresponding resolution unit is set to 0.

4. The method according to claim 3, characterized in that The process of determining the adaptive sliding window step for setting the 0 / 1 table is as follows: The 0 / 1 table is subjected to window-jumping sliding. If the number of 1s in the 0 / 1 table corresponding to the sliding window is equal to 0, the number of 1s in the current window is 0. If the number of 1s in the 0 / 1 table corresponding to the sliding window is not 0, the number of 1s in the current window is not 0, and the current accumulated number of 1s is the sum of the last accumulated number of 1s and the number of 1s in the current window. The window step of the window-jumping sliding is the window length, and the sliding window criterion is M / N, where M is the number of 1s that should be contained at least when the window contains the target, and N is the window length of the sliding window. After each window slide, if the current cumulative number of 1s is less than the number of 1s that should be contained when the window contains the target, continue to search with the jump window slide; if the current cumulative number of 1s is greater than or equal to the number of 1s that should be contained when the window contains the target, return one jump window for a refined search; In the refinement search process, if the number of 1s in the 0 / 1 table corresponding to the window is 1, the sliding window step of the window is the difference between the number of 1s that should be contained in the window when the target is contained and the number of 1s in the 0 / 1 table; If the number of 1s in the 0 / 1 table corresponding to the window is 2, then the sliding window step of the window is the difference between the number of 1s that should be contained in the window when the target is contained and the number of 1s in the 0 / 1 table; If the number of 1s in the 0 / 1 table corresponding to the window is 3, the sliding window step of the window is 1; If the number of 1s in the 0 / 1 table corresponding to the window is 4, the sliding window step of the window is 1; Repeat the above steps to get the expression of adaptive sliding window step: S = max(Mm, 1) Among them, S is the adaptive sliding window step, m is the number of 1s in the 0 / 1 table corresponding to the sliding window, and max is the maximum value function.

5. The method according to claim 4, characterized in that The process of determining the amplitude of each window detection point is as follows: Based on the amplitude at the corresponding position of the resolution unit of each window, the amplitude of the detection point of each window is determined.

6. The method according to claim 5, characterized in that The step of determining the amplitude of each window detection point based on the amplitude at the corresponding position of the resolution unit of each window includes: When the amplitude at the corresponding position of the central resolution unit of each window is not 0, the amplitude at the corresponding position of the central resolution unit of each window is used as the amplitude of the detection point; When the amplitude at the position corresponding to the central resolution unit of each window is 0, the maximum amplitude of the positions corresponding to the resolution units on both sides of the central resolution unit of each window is determined as the amplitude of the detection point; When the amplitude of the positions corresponding to the central resolution unit of each window and the positions corresponding to the resolution units on both sides are 0, the maximum amplitude of the positions corresponding to the resolution units on both sides and the positions corresponding to the resolution units on both sides are determined as the amplitude of the detection point.

7. A processor, characterized in that: The processor is used to run a program, wherein the program executes the method according to any one of claims 1 to 6 when running.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 6.

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