False point elimination method, device and equipment based on multi-detection-point direction finding

Abstract

The application provides a false point elimination method, device and equipment based on multi-detection point direction finding. The method comprises the following steps: acquiring multiple continuous echo signals, combining the multiple continuous echo signals to obtain to-be-processed data; wherein the to-be-processed data is data arranged according to distance dimensions and speed dimensions; sequentially performing digital beam forming, moving target detection and constant false alarm detection processing on the to-be-processed data to obtain multiple initial target points; jointly forming multiple to-be-processed input groups by the multiple initial target points and adjacent points of the multiple initial target points; performing angle measurement processing on the multiple to-be-processed input groups to obtain multiple initial target angle measurement results; deleting to-be-processed input groups in which initial target angle measurement results corresponding to the initial target points and the adjacent points of the initial target points in the multiple to-be-processed input groups are all greater than an angle threshold value to obtain final target points, and the accuracy of screening the final target points is improved by introducing more context information and spatial consistency constraints.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar signal processing, and particularly relates to a false point elimination method, device and equipment based on multi-detection point direction finding. BACKGROUND

[0002] As an important detection means in modern military and civilian fields, the performance of radar is directly related to the accuracy and real-time of target detection. In the process of radar signal processing, target signals often coexist with various noise, clutter and interference signals. These non-target signals pose a serious challenge to the normal operation of radar. Especially in complex environments such as cities, mountains or oceans, due to the complexity of topography, climate conditions and electromagnetic environment, the echo signals received by radar contain a large amount of background clutter and interference. These clutters not only increase the data processing burden of radar system, but also may cause the target signal to be submerged, thereby affecting the detection performance and target recognition ability of radar. Therefore, how to effectively suppress clutter and improve the accuracy and real-time of radar signal processing has become a key problem in the development of current radar technology.

[0003] In order to solve the problem of clutter suppression in radar signal processing, a variety of technical means have been widely used. Among them, moving target indication (MTI) and moving target detection (MTD) are two commonly used clutter suppression methods. MTI suppresses stationary clutter by designing a filter to improve the signal-to-noise ratio of radar, thereby realizing the detection of moving targets. MTD further distinguishes targets and clutters by using the Doppler difference between them relative to the radar, and optimizes the filter set at a certain point in the Doppler frequency band to achieve the best match, thereby improving the signal-to-noise ratio and target detection capability. In addition, the constant false alarm rate (CFAR) technology is also an important part of radar signal processing, which dynamically adjusts the threshold of target detection according to the received data of radar, so as to maximize the target detection probability under the condition of constant false alarm rate. These existing technologies have improved the clutter suppression ability and target detection performance of radar system to a certain extent.

[0004] Although existing radar signal processing algorithms have achieved certain results in clutter suppression and target detection, there are still many deficiencies. First, the complexity of environmental noise and clutter makes it difficult to guarantee the quality of radar signals, especially in complex environments. Existing algorithms often fail to completely suppress clutter, resulting in a large number of clutter signals being reported to the radar system. This not only increases the data processing burden of the radar system, but also may lead to misjudgment or missed judgment of target signals. Second, the existing algorithm has limited target recognition ability in complex environments. Background clutter and interference may make the target and background signals similar, making it difficult to accurately distinguish the target and clutter points, thereby causing the false alarm rate to rise. These problems not only affect the accuracy and real-time performance of the radar system, but also limit the application and development of radar technology in a wider range. Therefore, a more efficient and accurate radar signal processing algorithm is needed to deal with the challenges of clutter suppression and target detection in complex environments. SUMMARY

[0005] In order to solve the above problems existing in the prior art, the present application provides a false point elimination method and device based on multi-detection point direction finding.

[0006] The technical problem to be solved by the present application is solved by the following technical scheme:

[0007] In a first aspect, the present application provides a false point elimination method based on multi-detection point direction finding, comprising:

[0008] Obtaining a plurality of continuous echo signals and combining the plurality of continuous echo signals to obtain processed data; wherein the processed data is data arranged according to the distance dimension and the speed dimension obtained by using data padding processing;

[0009] Performing digital beam forming, moving target detection and constant false alarm detection processing on the processed data in turn to obtain a plurality of initial target points;

[0010] The plurality of initial target points and the adjacent points of the plurality of initial target points together form a plurality of input groups to be processed;

[0011] Performing angle measurement processing on the plurality of input groups to be processed to obtain a plurality of initial target angle measurement results;

[0012] Deleting the input groups to be processed in which the initial target angle measurement results corresponding to the initial target points and the adjacent points of the initial target points are all greater than the angle threshold value to obtain the final target points.

[0013] Optionally, obtaining a plurality of continuous echo signals and combining the plurality of continuous echo signals to obtain processed data comprises:

[0014] S201, obtaining a plurality of continuous echo signals;

[0015] S202, performing mixing filtering and AD sampling processing on the plurality of continuous echo signals to obtain AD sampling data;

[0016] S203, performing data padding processing on the AD sampling data to obtain AD padding data; the AD padding data is arranged according to a distance dimension;

[0017] S204, sequentially performing the steps of S201-S203 according to the order of the continuous wave signals transmitted by the radar to obtain to-be-processed data.

[0018] Optionally, the to-be-processed data is sequentially subjected to digital beam forming, moving target detection, and constant false alarm detection processing to obtain a plurality of initial target points, including:

[0019] The to-be-processed data is subjected to digital beam forming DBF processing to obtain beam information;

[0020] The beam information is subjected to moving target detection MTD processing to obtain moving target results;

[0021] The moving target results are subjected to constant false alarm detection processing by using SO-CFAR to obtain a plurality of initial target points.

[0022] Optionally, after the to-be-processed data is sequentially subjected to digital beam forming, moving target detection, and constant false alarm detection processing to obtain a plurality of initial target points, the method further includes:

[0023] The signal-to-noise ratios corresponding to the plurality of initial target points are sorted in descending order to obtain a sorting result;

[0024] The first N initial target points in the sorting result are taken as first screening target points.

[0025] Optionally, after the first N initial target points in the sorting result are taken as first screening target points, the method further includes:

[0026] The first screening target points are subjected to angle measurement processing to obtain screening point angle measurement results;

[0027] The first screening target points that do not meet an angle range in the screening point angle measurement results are deleted to obtain second screening target points; the angle range is an angle range of a beam in which the first screening target points are located in the beam information.

[0028] Optionally, the plurality of initial target points and adjacent points of the plurality of initial target points jointly constitute a plurality of to-be-processed input groups, including:

[0029] The second screening target points and adjacent points of the second screening target points jointly constitute a plurality of to-be-processed input groups.

[0030] Optionally, the multiple input groups to be processed are subjected to angle measurement processing to obtain multiple initial target angle measurement results, including:

[0031] The multiple input groups to be processed are subjected to angle measurement processing in a sum-difference beam scanning manner to obtain multiple initial target angle measurement results.

[0032] In a second aspect, the present application provides a false point elimination device based on multi-detection point direction finding, which comprises an acquisition unit, a processing unit, a construction unit, an angle measurement unit and a screening unit.

[0033] The acquisition unit is configured to acquire multiple continuous echo signals and combine the multiple continuous echo signals to obtain to-be-processed data; wherein the to-be-processed data is data arranged according to distance and velocity dimensions obtained by using data padding processing.

[0034] The processing unit is configured to sequentially perform digital beam forming, moving target detection and constant false alarm detection processing on the to-be-processed data to obtain multiple initial target points.

[0035] The construction unit is configured to jointly form multiple input groups to be processed by using the multiple initial target points and adjacent points of the multiple initial target points.

[0036] The angle measurement unit is configured to perform angle measurement processing on the multiple input groups to be processed to obtain multiple initial target angle measurement results.

[0037] The screening unit is configured to delete the input groups to be processed in which the initial target angle measurement results corresponding to the initial target points and the adjacent points of the initial target points are all greater than an angle threshold value to obtain final target points.

[0038] In a third aspect, the present application provides a false point elimination device based on multi-detection point direction finding, which comprises a processor, a storage medium and a bus, the storage medium stores machine readable instructions executable by the processor, when the false point elimination device based on multi-detection point direction finding is running, the processor communicates with the storage medium through the bus, and the processor executes the machine readable instructions to perform the steps of the false point elimination method based on multi-detection point direction finding of the first aspect.

[0039] This invention provides a method, apparatus, and device for false point removal based on multi-detection point direction finding. The method includes: acquiring multiple continuous echo signals and combining them to obtain data to be processed; wherein the data to be processed is data arranged according to the distance and velocity dimensions obtained through data completion processing; sequentially performing digital beamforming, moving target detection, and constant false alarm rate (CFAR) detection on the data to be processed to obtain multiple initial target points; forming multiple input groups to be processed by combining the multiple initial target points and their adjacent points; performing angle measurement processing on the multiple input groups to obtain multiple initial target angle measurement results; and deleting the input groups where the initial target point and its adjacent point's initial target angle measurement results are all greater than an angle threshold to obtain the final target point. In this invention, multiple continuous echo signals are combined to obtain the data to be processed. After performing preliminary constant false alarm rate (CFAR) detection on the data to be processed, an initial target point is obtained. The initial target point and its corresponding neighboring points are taken as a group of input points to be processed. The angle measurement of the points in each group of input points is compared. Since the angle measurement results of the real target point and its neighboring points are not much different, but the angle measurement results of the false point and its neighboring points are significantly different, the final target point can be determined based on the angle measurement results. That is, the method of this invention improves the accuracy and robustness of the final target point selection by introducing more contextual information and spatial consistency constraints.

[0040] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating a false point removal method based on multi-detection point direction finding, provided in an embodiment of the present invention.

[0042] Figure 2 An exemplary diagram illustrating the arrangement of the data to be processed is shown.

[0043] Figure 3 An exemplary simulation result diagram is shown using the existing target point screening process;

[0044] Figure 4 An exemplary simulation result diagram of the target point screening method of the present invention is shown;

[0045] Figure 5 An exemplary schematic diagram of another simulation result using an existing target point screening process is shown;

[0046] Figure 6 An exemplary simulation result diagram of the target point screening method of the present invention is shown;

[0047] Figure 7 A structure schematic diagram of a false point elimination device based on multi-detection point direction finding provided by an embodiment of the present application is shown in the figure.

[0048] Figure 8 A structure schematic diagram of a false point elimination device based on multi-detection point direction finding provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0049] The present application will be further described in detail below in combination with specific embodiments, but the embodiments of the present application are not limited thereto.

[0050] In order to improve the accuracy of the final target point screening, an embodiment of the present application provides a false point elimination method based on multi-detection point direction finding. Figure 1 A flowchart of a false point elimination method based on multi-detection point direction finding provided by an embodiment of the present application is shown in the figure, which includes: Figure 1

[0051] S101, a plurality of continuous echo signals are acquired, and the plurality of continuous echo signals are combined to obtain to-be-processed data.

[0052] The to-be-processed data is data arranged according to the distance dimension and the speed dimension obtained by using data padding processing.

[0053] Specifically, after the radar transmits continuous wave signals and encounters a target in space, the reflected echo signals are received by the receiving antenna, and a plurality of continuous echo signals are usually combined into a frame of data to form to-be-processed data for signal processing. Figure 2 An arrangement schematic diagram of the to-be-processed data is shown in the figure. Figure 2 As shown in the figure, the to-be-processed data is data containing two dimensions of distance dimension and speed dimension, and PRT represents the number of pulses transmitted by the radar per second. The combination process of the to-be-processed data is that: after the echo signal is subjected to frequency mixing filtering and AD sampling, the AD sampling data of a certain point is padded to an integer power of 2, and is used as one row of a frame of data. This row is the distance dimension of the data, and the above processing process is sequentially performed according to the order of the radar transmitting continuous wave signals, and is arranged according to the column to obtain the to-be-processed data.

[0054] It can be understood that the data is arranged according to the distance dimension and the speed dimension, and the subsequent processing process can be more efficient after appropriate padding processing. This data organization method helps to improve the performance of the radar system, especially for the detection and classification of moving targets.

[0055] Optionally, S101 can specifically include:

[0056] S201, a plurality of continuous echo signals are acquired. ​

[0057] S202, performing mixing filtering and AD sampling processing on the plurality of continuous echo signals to obtain AD sampling data.

[0058] S203, performing data padding processing on the AD sampling data to obtain AD padding data; the AD padding data is arranged according to a distance dimension.

[0059] S204, sequentially performing the steps of S201-S203 according to the order of the continuous wave signals transmitted by the radar to obtain to-be-processed data.

[0060] S102, sequentially performing digital beam forming, moving target detection and constant false alarm detection processing on the to-be-processed data to obtain a plurality of initial target points.

[0061] Optionally, S102 can specifically include:

[0062] performing digital beam forming DBF processing on the to-be-processed data to obtain beam information;

[0063] performing moving target detection MTD processing on the beam information to obtain moving target results;

[0064] performing constant false alarm detection processing on the moving target results by using SO-CFAR to obtain a plurality of initial target points.

[0065] In this embodiment, by using a digital beam forming DBF processing method, for to-be-processed data in a certain direction, phase difference caused by propagation wave difference due to different spatial positions of sensors is compensated, in-phase superposition is realized, maximum energy reception in the direction is realized, beam forming in the direction is completed, and useful expected signals are received. This kind of gathering of direction gain of array reception in a specified direction is equivalent to forming a "beam", and finally beam information is obtained.

[0066] Target detection MTD processing can use a Doppler filter set to suppress various clutters, thereby improving the ability of the radar to detect moving targets in a clutter background. The MTD technology distinguishes clutters and moving targets by the difference in Doppler frequency, can not only filter out clutters, but also distinguish targets with different moving speeds, thereby greatly improving the ability to detect moving targets in a clutter background.

[0067] S103, the plurality of initial target points and adjacent points of the plurality of initial target points jointly constitute a plurality of to-be-processed input groups.

[0068] It should be noted that the plurality of initial target points obtained after SO-CFAR constant false alarm detection processing are target points containing clutters and interference, and can be further subjected to screening processing.

[0069] Optionally, after S103, the method further includes:

[0070] Sort the signal-to-noise ratios corresponding to the plurality of initial target points in descending order to obtain a sorting result.

[0071] Take the first N initial target points in the sorting result as the first screening target points.

[0072] Specifically, a minimum signal-to-noise ratio threshold TH snr Assuming that there are M initial target points obtained after completing the SO-CFAR. Sort the signal-to-noise ratios of the M initial target points in descending order, and select the first N initial target points greater than the minimum signal-to-noise ratio threshold TH snr in the sorting result to obtain the first screening target points.

[0073] Optionally, after taking the first N initial target points in the sorting result as the first screening target points, the method further includes:

[0074] Perform angle measurement processing on the first screening target points to obtain screening point angle measurement results.

[0075] Delete the first screening target points that do not meet the angle range in the screening point angle measurement results to obtain second screening target points; the angle range is the angle range of the beam in which the first screening target points are located.

[0076] That is, in the present embodiment, if the screening point angle measurement result corresponding to the current first screening target point does not meet the angle range of the beam in which the first screening target point is located, the first screening target point is also deleted.

[0077] Optionally, S103 can further include:

[0078] The second screening target points and the adjacent points of the second screening target points jointly constitute a plurality of to-be-processed input groups.

[0079] S104, performing angle measurement processing on the plurality of to-be-processed input groups to obtain a plurality of initial target angle measurement results.

[0080] The embodiment of the present application provides a false point elimination method based on multi-detection-point direction finding, comprising: acquiring multiple continuous echo signals, and combining the multiple continuous echo signals to obtain to-be-processed data; wherein the to-be-processed data is data arranged according to distance dimension and speed dimension obtained by using data to fill in; sequentially performing digital beam forming, moving target detection and constant false alarm detection processing on the to-be-processed data to obtain multiple initial target points; jointly forming multiple to-be-processed input groups by the multiple initial target points and adjacent points of the multiple initial target points; performing angle measurement processing on the multiple to-be-processed input groups to obtain multiple initial target angle measurement results; deleting to-be-processed input groups in which initial target angle measurement results corresponding to the initial target points and the adjacent points of the initial target points are all greater than an angle threshold value, to obtain final target points. In the embodiment of the present application, the multiple continuous echo signals are combined to obtain the to-be-processed data, and the to-be-processed data is preliminarily subjected to constant false alarm detection to obtain the initial target points, the initial target points and the adjacent points corresponding to the initial target points are taken as a to-be-processed input group, and the angle measurement of the points in each to-be-processed input group is compared. Since the angle measurement results of the real target points and the adjacent points thereof are not greatly different, but the angle measurement results of the false points and the adjacent points thereof are greatly different, the final target points can be finally determined according to the angle measurement results, that is, the method of the present application improves the accuracy and robustness of the selection of the final target points by introducing more context information and spatial consistency constraints.

[0081] Optionally, S104 specifically can include:

[0082] The angle measurement processing on the multiple to-be-processed input groups is performed in a sum-difference beam scanning mode, to obtain the multiple initial target angle measurement results.

[0083] S105, deleting to-be-processed input groups in which initial target angle measurement results corresponding to the initial target points and the adjacent points of the initial target points are all greater than an angle threshold value in the multiple to-be-processed input groups, to obtain final target points.

[0084] Specifically, in the embodiment, to-be-processed input groups in which initial target angle measurement results corresponding to the initial target points and the adjacent points of the initial target points are all greater than an angle threshold value in the multiple to-be-processed input groups are deleted, to obtain final processing input groups, and the initial target points corresponding to the final processing input groups are taken as the final target points.

[0085] Wherein, the angle threshold value can be determined according to the mean μ and the variance σ of the initial target angle measurement results of each to-be-processed input group 2 In the embodiment, the angle threshold value can be exemplarily μ+2σ 2As the angle threshold corresponding to the input group to be processed, specifically, by analyzing the statistical characteristics (mean and variance, etc.) of a group of point traces (input group to be processed), the noise distribution can be better understood, and the angle threshold can be set accordingly. This helps to distinguish between real target point traces and random fluctuations caused by noise, further improving the anti-noise performance of the system.

[0086] It can be understood that in the embodiment, the initial target angle measurement result corresponding to the initial target point and the adjacent point of the initial target point is used for screening the final target point, which can increase the spatial consistency constraint. That is, the angle consistency verification. Specifically, because the real target usually shows relatively stable angle characteristics in continuous radar scanning. By comparing the angles of multiple point traces in the same group, it can be verified whether these point traces come from the same physical target. If the angle of a point trace is significantly different from the angles of the surrounding adjacent points, it is likely to be a false point trace caused by noise or clutter. In addition, for a moving target, its position and angle should have a certain continuity and coherence. By comparing the angle changes of adjacent point traces, abnormal jumps or mutations can be detected, thereby excluding those point traces that do not conform to logic.

[0087] It should be noted that the adjacent points of the initial target point in the embodiment can be the point traces adjacent to the initial target point in the up, down, left and right directions.

[0088] In order to verify the effectiveness of the false point elimination method based on multi-detection point direction finding provided by the embodiment of the application, a simulation experiment is also performed.

[0089] The simulation uses a 24G linear frequency modulated continuous wave radar to collect data of a UAV, and the collected data is subjected to target point screening according to the existing target point screening process (the collected data is sequentially subjected to MTI, MTD and CFAR processing) and the false point elimination method provided by the application. Specifically, the 24G linear frequency modulated continuous wave radar is used to collect five consecutive frames of point traces. Based on the five consecutive frames of point traces, the two screening methods are compared. Figure 3 An exemplary simulation result diagram using the existing target point screening process is shown. Figure 4 An exemplary simulation result diagram using the target point screening method of the application is shown. Figure 3 And Figure 4 By comparing, it can be seen that the false points can be obviously eliminated using the method of the application.

[0090] Based on the above simulation experiment, the UAV is vertically flown upward, and the above two methods are still used for target point screening. Figure 5 Another exemplary simulation result diagram using the existing target point screening process is shown. Figure 6Another simulation result schematic diagram using the target point screening method of the present application is shown as an example. Figure 5 And Figure 6 It can be seen that the method has better clutter removal effect and can more accurately report the target track.

[0091] The method provided by the embodiments of the present application can be applied to an electronic device. Specifically, the electronic device can be a desktop computer, a portable computer, a smart mobile terminal, a server, etc., and the embodiments of the present application are not limited thereto.

[0092] Based on the same inventive concept, the embodiments of the present application further provide a false point removal device based on multi-detection point direction finding. Figure 7 A structural schematic diagram of a false point removal device based on multi-detection point direction finding provided by the embodiments of the present application is shown in FIG. 7. Figure 7 As shown in FIG. 7, the device includes an acquisition unit 701, a processing unit 702, a construction unit 703, an angle measurement unit 704, and a screening unit 705.

[0093] The acquisition unit 701 is configured to acquire a plurality of continuous echo signals and combine the plurality of continuous echo signals to obtain to-be-processed data, wherein the to-be-processed data is data arranged according to a distance dimension and a velocity dimension obtained by using data padding processing.

[0094] The processing unit 702 is configured to sequentially perform digital beam forming, moving target detection, and constant false alarm detection processing on the to-be-processed data to obtain a plurality of initial target points.

[0095] The construction unit 703 is configured to jointly form a plurality of to-be-processed input groups from the plurality of initial target points and adjacent points of the plurality of initial target points.

[0096] The angle measurement unit 704 is configured to perform angle measurement processing on the plurality of to-be-processed input groups to obtain a plurality of initial target angle measurement results.

[0097] The screening unit 705 is configured to delete to-be-processed input groups in which initial target angle measurement results corresponding to the initial target points and the adjacent points of the initial target points in the plurality of to-be-processed input groups are all greater than an angle threshold value to obtain final target points.

[0098] Figure 8A structural diagram of a false point elimination device based on multi-detection point direction finding provided by an embodiment of the present application includes a processor 810, a storage medium 820, and a bus 830. The storage medium 820 stores machine readable instructions executable by the processor 810. When the false point elimination device based on multi-detection point direction finding is running, the processor 810 communicates with the storage medium 820 through the bus 830. The processor 810 executes the machine readable instructions to perform the steps of the method embodiments described above. The specific implementation and technical effects are similar, and will not be described here.

[0099] The storage medium can include a random access memory (RAM) and can also include a non-volatile memory (NVM), such as at least one disk memory. Optionally, the storage medium can also be at least one storage device located away from the aforementioned processor.

[0100] The processor described above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc. It can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0101] It should be noted that the terms "first", "second", and the like are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The implementation described in the following exemplary embodiments does not represent all implementations consistent with the present application. Rather, they are merely examples of devices and methods consistent with some aspects of the present application.

[0102] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the description of the specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described can be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in the specification.

[0103] Although the present application is described herein in conjunction with various embodiments, those skilled in the art, with reference to the appended drawings and the disclosure, can understand and implement other variations of the disclosed embodiments in implementing the claimed application. In the description of the present application, the word "comprising" does not exclude other components or steps, "a" or "one" does not exclude a plurality, and "plurality" means two or more, unless otherwise expressly specified. In addition, some measures are described in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0104] The above is a further detailed description of the present application in conjunction with specific preferred embodiments, and cannot be considered as limiting the specific implementation of the present application to these descriptions. For those skilled in the art, without departing from the concept of the present application, a number of simple deductions or substitutions can be made, which should be considered as falling within the scope of protection of the present application.

Claims

1. A method for eliminating false points based on multi-detection point direction finding, characterized in that, include: Multiple continuous echo signals are acquired and combined to obtain data to be processed; wherein, the data to be processed is data arranged according to the distance dimension and the velocity dimension obtained by data completion processing; Digital beamforming, moving target detection, and constant false alarm rate (CFAR) detection are sequentially performed on the data to be processed to obtain multiple initial target points; The multiple initial target points and their adjacent points together constitute multiple input groups to be processed; Angle measurement processing is performed on the multiple input groups to be processed to obtain multiple initial target angle measurement results; In the plurality of input groups to be processed, the input groups whose initial target angle measurement results corresponding to the initial target point and the adjacent points of the initial target point are all greater than the angle threshold are deleted to obtain the final target point; Specifically, angle measurement processing is performed on the multiple input groups to be processed to obtain multiple initial target angle measurement results, including: Angle measurement is performed on the multiple input groups to be processed using a sum-difference beam scanning method to obtain the angle measurement results of the multiple initial targets.

2. The false point removal method based on multi-detection point direction finding according to claim 1, characterized in that, The process of acquiring multiple continuous echo signals and combining the multiple continuous echo signals to obtain the data to be processed includes: S201. Acquire the plurality of continuous echo signals; S202. Perform frequency mixing filtering and AD sampling processing on the multiple continuous echo signals to obtain AD sampling data; S203. Perform data completion processing on the AD sampling data to obtain AD completed data; the AD completed data is arranged according to the distance dimension. S204. Execute steps S201-S203 sequentially according to the order of the continuous wave signals emitted by the radar to obtain the data to be processed.

3. The false point removal method based on multi-detection point direction finding according to claim 1, characterized in that, The process of sequentially performing digital beamforming, moving target detection, and constant false alarm rate (CFAR) detection on the data to be processed yields multiple initial target points, including: Digital beamforming (DBF) processing is performed on the data to be processed to obtain beam information; The beam information is processed by Moving Target Detection (MTD) to obtain the moving target result; The moving target results are processed using SO-CFAR with constant false alarm rate (CFAR) to obtain the multiple initial target points.

4. The false point removal method based on multi-detection point direction finding according to claim 3, characterized in that, After sequentially performing digital beamforming, moving target detection, and constant false alarm rate (CFAR) detection on the data to be processed to obtain multiple initial target points, the process further includes: The signal-to-noise ratios corresponding to the multiple initial target points are sorted in descending order to obtain the sorting result; Take the first N initial target points from the sorting results as the first screening target points.

5. The false point removal method based on multi-detection point direction finding according to claim 4, characterized in that, After selecting the first N initial target points from the sorting results as the first selection target points, the process further includes: Angle measurement is performed on the first target point to obtain the angle measurement result of the target point; The first screening target point that does not conform to the angle range in the angle measurement results of the screening point is deleted to obtain the second screening target point; the angle range is the angle range of the beam in which the first screening target point is located in the beam information.

6. The false point removal method based on multi-detection point direction finding according to claim 5, characterized in that, The step of forming multiple input groups to be processed by combining the multiple initial target points and their adjacent points includes: The second target point and its adjacent points are combined to form the plurality of input groups to be processed.

7. A false point removal device based on multi-detection point direction finding, characterized in that, The false point elimination device based on multi-detection point orientation includes: an acquisition unit, a processing unit, a construction unit, an angle measurement unit, and a filtering unit; The acquisition unit is used to: acquire multiple continuous echo signals and combine the multiple continuous echo signals to obtain data to be processed; wherein, the data to be processed is data arranged according to the distance dimension and the velocity dimension obtained by data completion processing; The processing unit is used to: sequentially perform digital beamforming, moving target detection, and constant false alarm rate (CFAR) detection on the data to be processed to obtain multiple initial target points; The construction unit is used to: combine the plurality of initial target points and the adjacent points of the plurality of initial target points to form a plurality of input groups to be processed; The angle measurement unit is used to: perform angle measurement processing on the multiple input groups to be processed, and obtain multiple initial target angle measurement results; The filtering unit is used to: delete the input groups of the plurality of input groups to be processed in which the initial target point and the initial target point's adjacent points have initial target angle measurement results that are all greater than an angle threshold, so as to obtain the final target point; The angle measurement unit is specifically used to: perform angle measurement processing on the multiple input groups to be processed using a sum and difference beam scanning method to obtain the multiple initial target angle measurement results.

8. A false point removal device based on multi-detection point direction finding, characterized in that, include: The device includes a processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the spurious point removal device based on multi-detection point orientation is running, the processor communicates with the storage medium via the bus. The processor executes the machine-readable instructions to perform the steps of the spurious point removal method based on multi-detection point orientation as described in any one of claims 1-6.

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