Image elimination method and device, electronic device, and storage medium

Abstract

The present disclosure relates to the field of distributed multi-sensor signal joint detection, and provides a mirror elimination method and device, electronic equipment, and storage medium. The method includes: constructing the center position of a virtual node of a distributed collaborative detection system; dividing the common view area space into multiple spatial grids, aligning each radar channel signal with each spatial grid, and determining the target grid array; constructing a comprehensive virtual node distance function based on the relationship between the position of the target grid with the same signal-to-noise ratio value and the center position of the virtual node; constructing a comprehensive virtual array face pointing deviation angle function based on the deviation angle of the array face pointing of the target grid with the same signal-to-noise ratio value and each sensor node; constructing a threat degree evaluation function using the above two functions, evaluating the target threat degree of the target grid with the same signal-to-noise ratio value, and selecting the information corresponding to the target grid with the highest target threat degree as the real target information. The present disclosure can effectively improve the accuracy of mirror elimination and the detection efficiency of the distributed collaborative detection system.

Description

Technical Field

[0001] The present disclosure relates to the technical field of distributed multi-sensor signal joint detection, and in particular to a mirror image elimination method and device, electronic equipment, and storage medium. Background Art

[0002] Gridded joint detection is a radar multi-channel signal fusion detection method that divides the space into numerous spatial grids. According to the relative radar position and two-way signal delay of each spatial grid, the echo signal range unit of each channel is matched to each spatial grid to complete signal registration and fusion. Then, the target is detected according to the fusion signal of each spatial grid to complete joint detection.

[0003] Due to the different positions and viewing angles of the radars, the shapes of the spatial units are complex and irregular. Therefore, the spatial grid unit can only simulate the spatial unit approximately, but cannot completely simulate the spatial unit. This results in the target signal being registered to multiple spatial grids near the target's true position during the signal spatiotemporal registration process, forming multiple target mirror images with the same signal-to-noise ratio (SNR) value as the real target, such as Figure 1 Therefore, in order to improve the detection performance, it is necessary to further identify the real target and the target mirror image.

[0004] The existing grid detection mirror elimination method usually selects the first grid as the real target according to the grid storage order. Figure 2 ,Existing grid detection image elimination methods usually include the following basic steps:

[0005] Step S00: aligning radar signals of each channel to each spatial grid.

[0006] Step S01: Count the SNR values ​​of each grid, each channel, and the sum of the SNR values ​​of all channels to prepare for screening grids with targets.

[0007] Step S02: Filter target grids that meet the requirements according to a preset SNR threshold and a threshold value of the number of channels exceeding the SNR threshold.

[0008] Step S03: sort the target grids that meet the requirements in descending order of SNR and value.

[0009] Step S04: Save the first grid information to the target set in the storage order, clear the radar echo values ​​of each channel corresponding to the grid, and remove the grid information from the target grid.

[0010] Step S05: recalculate the sum of the SNR value of each target grid and the SNR values ​​of all channels.

[0011] Step S06: Repeat steps S02 to S04 until there is no target grid that meets the conditions.

[0012] Step S07: At this point, the grid information in the target set is the real target information.

[0013] However, the above-mentioned grid detection image elimination method only takes the first grid arranged in the grid storage order as the real target, lacks an effective screening basis for selecting the real target, and may cause inaccurate image elimination and reduce detection efficiency. Summary of the Invention

[0014] The present disclosure aims to solve at least one of the problems existing in the prior art and provides a method and device for image removal, an electronic device, and a storage medium.

[0015] In one aspect of the present disclosure, a method for eliminating mirror images is provided, the method comprising:

[0016] According to the distribution of each sensor node in the distributed collaborative detection system, a virtual node center position corresponding to the distributed collaborative detection system is constructed;

[0017] reading echo data from each radar channel of the distributed collaborative detection system, dividing a common view area space corresponding to each radar channel echo data into a plurality of spatial grids, registering each radar channel signal with each of the spatial grids based on the echo data of each radar channel, and determining a target grid array according to the registration results;

[0018] Constructing a comprehensive virtual node distance function based on the relationship between the position of the target grid with the same signal-to-noise ratio value in the target grid array and the center position of the virtual node;

[0019] Based on the deviation angles between the target grids with the same signal-to-noise ratio value in the target grid array and the array faces of the sensor nodes, a comprehensive virtual array face deviation angle function is constructed;

[0020] Using the comprehensive virtual node distance function and the comprehensive virtual array pointing deviation angle function, a grid target threat assessment function is constructed;

[0021] The target threat degree of target grids with the same signal-to-noise ratio value in the target grid array is respectively evaluated using the grid target threat degree evaluation function, and information corresponding to the target grid with the highest target threat degree is selected as the true target information.

[0022] Optionally, constructing a virtual node center position corresponding to the distributed collaborative detection system according to the distribution of each sensor node in the distributed collaborative detection system includes:

[0023] Acquire location information of each sensor node, wherein the location information includes latitude, longitude, and altitude;

[0024] According to the position information and the importance of each sensor node to the distributed collaborative detection system, the virtual node center position is determined, and the virtual node center position includes the latitude of the virtual node center. ,longitude ,high Respectively expressed as:

[0025] ;

[0026] in, They represent the latitudes of the 1st, 2nd,…,Nrth sensor nodes respectively; They represent the longitudes of the 1st, 2nd,…,Nrth sensor nodes respectively; Represent the heights of the 1st, 2nd,…, Nrth sensor nodes respectively; They represent the critical weights of the 1st, 2nd, ..., Nrth sensor nodes respectively, and are used to indicate the importance of the 1st, 2nd, ..., Nrth sensor nodes to the distributed collaborative detection system; Nr represents the total number of sensor nodes in the distributed collaborative detection system.

[0027] Optionally, reading echo data of each radar channel of the distributed collaborative detection system, dividing a common view area space corresponding to each radar channel echo data into a plurality of spatial grids, registering each radar channel signal with each of the spatial grids based on the radar channel echo data, and determining a target grid array according to the registration result includes:

[0028] Read the echo data of each radar channel and store it in array;

[0029] According to the preset longitude interval , latitude interval , height interval , dividing the common view area space into a plurality of the spatial grids;

[0030] According to the azimuth, pitch angle and distance corresponding to each spatial grid and each sensor node, the corresponding position of each spatial grid is calculated. The range unit in the array completes the registration of each radar channel signal with each spatial grid;

[0031] According to the registration results, the signal-to-noise ratio values ​​of the radar channel signals corresponding to each of the spatial grids are counted, and the sum of the signal-to-noise ratio values ​​of all the radar channel signals corresponding to each of the spatial grids is calculated; wherein, the signal-to-noise ratio value of the td-th radar channel signal corresponding to the i-th spatial grid is expressed as , the sum of the signal-to-noise ratio values ​​of all radar channel signals corresponding to the i-th spatial grid is expressed as ,and , N represents the total number of radar channel signals corresponding to the i-th spatial grid;

[0032] Compare the signal-to-noise ratio values ​​of the radar channel signals corresponding to the spatial grids with the preset signal-to-noise ratio thresholds. Compare and count the number of over-threshold channels corresponding to each of the spatial grids; wherein the number of over-threshold channels corresponding to the spatial grid is used to indicate that the signal-to-noise ratio value in each radar channel signal corresponding to the spatial grid exceeds the signal-to-noise ratio threshold The number of radar channel signals; the number of over-threshold channels corresponding to the i-th spatial grid is expressed as ;

[0033] The sum of the signal-to-noise ratio values ​​of all radar channel signals corresponding to each of the spatial grids is compared with the preset signal-to-noise ratio value sum threshold. Compare and compare the number of over-threshold channels corresponding to each of the spatial grids with the preset channel number threshold Compare and select the corresponding signal-to-noise ratio value whose sum is greater than or equal to the signal-to-noise ratio value sum threshold And the corresponding number of channels that pass the threshold is greater than or equal to the channel number threshold The spatial grid is used as the target grid, and the target grid is stored in the target grid array ; Wherein, the target grid array The i-th element in Expressed as , i is the target grid array The element index of Represents the target raster arrays respectively The center longitude, center latitude, and center height of the spatial grid corresponding to the i-th element in , Represents the target raster arrays respectively The signal-to-noise ratio values ​​of the 1st, 2nd, …, Nth radar channel signals corresponding to the spatial grid corresponding to the i-th element in .

[0034] Optionally, constructing a comprehensive virtual node distance function based on the relationship between the position of the target grid with the same signal-to-noise ratio value in the target grid array and the center position of the virtual node includes:

[0035] According to the sum of the signal-to-noise ratio , for the target raster array Sort the elements in in descending order;

[0036] Select the target grid array The target grids with the same signal-to-noise ratio value as the first element are selected as candidate grids to form a candidate grid group;

[0037] According to the following formula, the comprehensive virtual node distance function corresponding to the i-th candidate grid in the candidate grid group is constructed: :

[0038] ;

[0039] in, Indicates the maximum value of the distance between the position of each candidate grid in the candidate grid group and the center position of the virtual node.

[0040] Optionally, constructing a comprehensive virtual array face pointing deviation angle function based on the deviation angles between the target grids with the same signal-to-noise ratio value in the target grid array and the array face pointing of each sensor node includes:

[0041] According to the following formula, the comprehensive virtual array pointing deviation angle function corresponding to the i-th candidate grid in the candidate grid group is constructed: :

[0042] ;

[0043] in, They represent the deviation angles between the i-th candidate grid and the array direction of the 1st, 2nd, ..., Nr-th sensor nodes in the candidate grid group respectively; It represents the maximum value of the deviation angle between each candidate grid in the candidate grid group and the array direction of each sensor node.

[0044] Optionally, constructing a grid target threat assessment function by using the integrated virtual node distance function and the integrated virtual array pointing deviation angle function includes:

[0045] According to the following formula, the grid target threat evaluation function corresponding to the i-th candidate grid in the candidate grid group is constructed: :

[0046] .

[0047] Optionally, the using the grid target threat degree evaluation function to respectively evaluate the target threat degrees of target grids with the same signal-to-noise ratio value in the target grid array, and selecting information corresponding to the target grid with the highest target threat degree as the true target information, includes:

[0048] Using the grid target threat assessment function , respectively evaluating the target threat degree of each candidate grid in the candidate grid group;

[0049] Arrange the candidate grids in the candidate grid group in descending order according to the target threat level, and select the candidate grid with the largest target threat level as the final grid and store it in the target set. , and the corresponding The echo values ​​of each radar channel in the array are cleared to zero, and the target grid array is Clear the corresponding information of the final grid;

[0050] according to Array to recount the target grid array The signal-to-noise ratio value of each radar channel signal corresponding to each target grid in and the sum of the signal-to-noise ratio values ​​of all radar channel signals;

[0051] The signal-to-noise ratio values ​​of the radar channel signals corresponding to the re-stated target grids are compared with the signal-to-noise ratio thresholds. Compare and count the number of channels that pass the threshold corresponding to each target grid;

[0052] The sum of the signal-to-noise ratio values ​​of all radar channel signals corresponding to the re-counted target grids is respectively compared with the sum threshold of the signal-to-noise ratio value Compare and compare the number of over-threshold channels corresponding to each target grid with the channel number threshold Compare and filter the corresponding signal-to-noise ratio values ​​to be greater than or equal to the signal-to-noise ratio value sum threshold And the corresponding number of channels that pass the threshold is greater than or equal to the channel number threshold If the result of the filter is empty, the target grid will be The information of the final grid in is used as the real target information.

[0053] Another aspect of the present disclosure provides a mirror image elimination device, comprising:

[0054] A virtual node construction module is used to construct a virtual node center position corresponding to the distributed collaborative detection system according to the distribution of each sensor node in the distributed collaborative detection system;

[0055] a registration module, configured to read the echo data of each radar channel of the distributed collaborative detection system, divide the common view area space corresponding to the echo data of each radar channel into a plurality of spatial grids, register each radar channel signal with each of the spatial grids based on the echo data of each radar channel, and determine a target grid array according to the registration results;

[0056] A distance function construction module, configured to construct a comprehensive virtual node distance function based on a relationship between the positions of target grids with the same signal-to-noise ratio value in the target grid array and the center positions of the virtual nodes;

[0057] A deflection angle function construction module is used to construct a comprehensive virtual array face pointing deviation angle function based on the deviation angles between the target grids with the same signal-to-noise ratio value in the target grid array and the array face pointing of each sensor node;

[0058] A threat assessment function construction module is used to construct a grid target threat assessment function using the comprehensive virtual node distance function and the comprehensive virtual array pointing deviation angle function;

[0059] The selection module is used to use the grid target threat degree evaluation function to respectively evaluate the target threat degrees of target grids with the same signal-to-noise ratio value in the target grid array, and select the information corresponding to the target grid with the highest target threat degree as the real target information.

[0060] Another aspect of the present disclosure provides an electronic device, including:

[0061] at least one processor; and,

[0062] a memory communicatively connected to at least one processor; wherein,

[0063] The memory stores instructions that can be executed by at least one processor. The instructions are executed by the at least one processor so that the at least one processor can perform the image elimination method described above.

[0064] Another aspect of the present disclosure provides a computer-readable storage medium storing a computer program, which implements the aforementioned image elimination method when executed by a processor.

[0065] Compared with the prior art, the present disclosure provides a screening criterion, namely, target threat degree, for eliminating mirror targets, which can effectively improve the accuracy of mirror elimination and enable the distributed collaborative detection system to obtain better detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] One or more embodiments are exemplarily illustrated by pictures in the corresponding drawings, and these exemplifications do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0067] Figure 1 A distribution diagram of target images in the prior art provided by the present disclosure;

[0068] Figure 2A flowchart of the existing grid detection image elimination method provided by the present disclosure;

[0069] Figure 3 A flowchart of a mirror image elimination method provided in one embodiment of the present disclosure;

[0070] Figure 4 A flowchart of a mirror image elimination method provided in another embodiment of the present disclosure;

[0071] Figure 5 A schematic structural diagram of an image elimination device provided in another embodiment of the present disclosure;

[0072] Figure 6 A schematic structural diagram of an electronic device provided in another embodiment of the present disclosure. DETAILED DESCRIPTION

[0073] In order to make the purpose, technical solutions and advantages of the embodiments of the present disclosure clearer, the embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. However, it will be understood by those skilled in the art that in each embodiment of the present disclosure, many technical details are provided to enable readers to better understand the present disclosure. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in the present disclosure can be implemented. The division of the following embodiments is for the convenience of description and should not constitute any limitation on the specific implementation of the present disclosure. The various embodiments can be combined and referenced with each other under the premise that there is no contradiction.

[0074] One embodiment of the present disclosure relates to a method for eliminating mirror images, the process of which is as follows: Figure 3 As shown, it includes steps S110 to S160. Figure 4 , steps S110 to S160 are described in detail.

[0075] Step S110 : constructing a virtual node center position corresponding to the distributed collaborative detection system according to the distribution of each sensor node in the distributed collaborative detection system.

[0076] Specifically, the distributed collaborative detection system may include a plurality of distributedly arranged sensor nodes and a control center in communication with each sensor node. Each sensor node performs distributed collaborative detection of a target under the control of the control center. The sensor nodes in the distributed collaborative detection system may be radar nodes.

[0077] For example, assuming that the total number of sensor nodes in the distributed collaborative detection system is Nr, and the location information of each sensor node including latitude, longitude, and altitude is known, step S110 may include: obtaining the location information of each sensor node. According to the location information and the importance of each sensor node to the distributed collaborative detection system, the virtual node center position is determined, and the virtual node center position includes the latitude of the virtual node center. ,longitude ,high Respectively expressed as: .

[0078] in, They represent the latitudes of the 1st, 2nd,…, Nrth sensor nodes respectively. They represent the longitudes of the 1st, 2nd,…, Nrth sensor nodes respectively. Represent the heights of the 1st, 2nd,…, Nrth sensor nodes respectively. Respectively represent the critical weights of the 1st, 2nd, ..., Nrth sensor nodes, which are used to indicate the importance of the 1st, 2nd, ..., Nrth sensor nodes to the distributed collaborative detection system, and are real numbers in [0, 1]. The critical weight of the nrth sensor node can be expressed as , the value range of nr is 1, 2,…, Nr.

[0079] It should be noted that, in this embodiment, the latitude, longitude, and altitude of the virtual node center, including the virtual node center position, can be determined in advance in an offline manner so as to be directly used in the subsequent mirror elimination process.

[0080] Step S120: read the echo data of each radar channel of the distributed collaborative detection system, divide the common view area space corresponding to the echo data of each radar channel into multiple spatial grids, align each radar channel signal with each spatial grid based on the echo data of each radar channel, and determine the target grid array according to the alignment result.

[0081] Specifically, step S120 mainly registers the echo data of each radar channel with each spatial grid, and determines the target grid array according to the registration result.

[0082] Exemplarily, step S120 includes the following steps S121 to S123.

[0083] Step S121, read the echo data of each radar channel and store it in Array. According to the preset longitude interval , latitude interval , height interval , the common view area is divided into multiple spatial grids. According to the azimuth, pitch angle and distance corresponding to each spatial grid and each sensor node, the corresponding spatial grid is calculated respectively. The range units in the array complete the registration of each radar channel signal with each spatial grid.

[0084] Step S122 : According to the registration result, the signal-to-noise ratio value of each radar channel signal corresponding to each spatial grid is counted, and the sum of the signal-to-noise ratio values ​​of all radar channel signals corresponding to each spatial grid is calculated.

[0085] Among them, the signal-to-noise ratio value of the td-th radar channel signal corresponding to the i-th spatial grid is expressed as ,and . Represents the signal amplitude of the tdth radar channel signal corresponding to the i-th spatial grid. The noise amplitude of the tdth radar channel signal corresponding to the i-th spatial grid is represented by the sum of the signal-to-noise ratio values ​​of all radar channel signals corresponding to the i-th spatial grid. ,and , N represents the total number of radar channel signals corresponding to the i-th spatial grid.

[0086] Step S123: Based on the preset signal-to-noise ratio value sum threshold , signal-to-noise ratio threshold and channel number threshold Filter out target grids that meet the requirements and obtain a target grid array. Step S123 specifically includes: comparing the signal-to-noise ratio values ​​of each radar channel signal corresponding to each spatial grid with the preset signal-to-noise ratio threshold. Compare and count the number of over-threshold channels corresponding to each spatial grid; compare the sum of the signal-to-noise ratio values ​​of all radar channel signals corresponding to each spatial grid with the preset signal-to-noise ratio value sum threshold. Compare and compare the number of over-threshold channels corresponding to each spatial grid with the preset channel number threshold Compare and select the corresponding signal-to-noise ratio value whose sum is greater than or equal to the signal-to-noise ratio value sum threshold And the corresponding number of channels that pass the threshold is greater than or equal to the channel number threshold The spatial grid is used as the target grid and the target grid is stored in the target grid array. .

[0087] Among them, the number of over-threshold channels corresponding to the spatial grid is used to indicate that the signal-to-noise ratio value of each radar channel signal corresponding to the spatial grid exceeds the signal-to-noise ratio threshold. The number of radar channel signals that exceed the threshold corresponding to the i-th spatial grid is expressed as In other words, step S123 can be to sum the signal-to-noise ratio values ​​of all radar channel signals corresponding to the i-th spatial grid Sum threshold of signal-to-noise ratio Compare and calculate the number of over-threshold channels corresponding to the i-th spatial grid and channel number threshold Compare, if satisfied and , then the i-th spatial grid is stored as the target grid in the target grid array .

[0088] Destination raster array The i-th element in Expressed as . i is the target grid array The element index of . Represents the target raster arrays respectively The center longitude, center latitude, and center altitude of the spatial grid corresponding to the i-th element in . Represents the target raster arrays respectively The signal-to-noise ratio values ​​of the 1st, 2nd, ..., Nth radar channel signals corresponding to the spatial grid corresponding to the i-th element in . Among them, the target grid array The signal-to-noise ratio value of the tdth radar channel signal corresponding to the spatial grid corresponding to the i-th element in can be expressed as .

[0089] Step S130 : constructing a comprehensive virtual node distance function based on the relationship between the positions of target grids with the same signal-to-noise ratio value in the target grid array and the center positions of the virtual nodes.

[0090] Exemplarily, step S130 includes: according to the sum of the signal-to-noise ratio values , for the target raster array Sort the elements in descending order. Select the target grid array The target grids with the same signal-to-noise ratio as the first element are selected as candidate grids to form a candidate grid group. According to the following formula, the comprehensive virtual node distance function corresponding to the i-th candidate grid in the candidate grid group is constructed: :

[0091] .

[0092] in, Indicates the maximum distance between the position of each candidate grid in the candidate grid group and the center position of the virtual node. Assuming that there are M candidate grids in the candidate grid group, the value of i ranges from 1 to M.

[0093] Step S140 , constructing a comprehensive virtual array face pointing deviation angle function based on the deviation angles of the array faces of the target grids with the same signal-to-noise ratio value in the target grid array and each sensor node.

[0094] Exemplarily, step S140 includes: constructing a comprehensive virtual array pointing deviation angle function corresponding to the i-th candidate grid in the candidate grid group according to the following formula: :

[0095] .

[0096] in, They represent the deviation angles between the ith candidate grid and the array direction of the 1st, 2nd, …, Nrth sensor nodes in the candidate grid group. The deviation angle between the ith candidate grid and the array direction of the nrth sensor node in the candidate grid group can be expressed as . It represents the maximum value of the deviation angle between each candidate grid in the candidate grid group and the array direction of each sensor node.

[0097] Step S150 , constructing a grid target threat assessment function using the integrated virtual node distance function and the integrated virtual array pointing deviation angle function.

[0098] Exemplarily, step S150 includes: constructing a grid target threat evaluation function corresponding to the i-th candidate grid in the candidate grid group according to the following formula: :

[0099] .

[0100] Specifically, the grid target threat assessment function According to the integrated virtual array pointing deviation angle function Distance function to comprehensive virtual node The ratio of is constructed, so we have:

[0101] .

[0102] Step S160 , using a grid target threat evaluation function to respectively evaluate the target threat levels of target grids with the same signal-to-noise ratio in the target grid array, and selecting information corresponding to the target grid with the highest target threat level as true target information.

[0103] Specifically, step S160 is mainly based on the target threat level to give priority to selecting the target with high threat level to the distributed collaborative detection system as the real target, and complete the elimination of the target mirror.

[0104] Exemplarily, step S160 includes the following steps S161 to S164.

[0105] Step S161: Using the grid target threat assessment function , evaluate the target threat level of each candidate grid in the candidate grid group respectively. Arrange the candidate grids in the candidate grid group in descending order according to the target threat level, and select the candidate grid with the largest target threat level as the final grid and store it in the target set , and the final grid corresponds to The echo values ​​of each radar channel in the array are cleared to zero, and the target grid array is Clear the corresponding information of the final grid.

[0106] Among them, the target set It can be expressed as , V represents the target set The number of elements in , each element corresponds to a target, so V also represents the number of targets. The v Elements It can be expressed as . Represents the target set The v The center longitude, center latitude, and center altitude of the final grid corresponding to each element. Represents the target set The v The sum of the signal-to-noise ratio values ​​of all radar channel signals corresponding to the final grid corresponding to the elements. Represents the target set The v The signal-to-noise ratio values ​​of the 1st, 2nd, ..., Nth radar channel signals corresponding to the final grid elements. The v The signal-to-noise ratio value of the tdth radar channel signal corresponding to the final grid corresponding to the elements can be expressed as .

[0107] Step S162, according to Array to recount the target grid array The signal-to-noise ratio value of each radar channel signal corresponding to each target grid in and the sum of the signal-to-noise ratio values ​​of all radar channel signals.

[0108] Specifically, step S162 can be based on Array, re-count the target grid array The signal-to-noise ratio value corresponding to the i-th target grid in Sum of the signal-to-noise ratio .

[0109] Step S163 is mainly used to calculate the signal-to-noise ratio value based on the preset threshold. , signal-to-noise ratio threshold and channel number threshold Filter out target grids that meet the requirements. Step S163 specifically includes: comparing the signal-to-noise ratio values ​​of each radar channel signal corresponding to each target grid that has been re-counted with the signal-to-noise ratio threshold. Compare and count the number of over-threshold channels corresponding to each target grid, and compare the sum of the signal-to-noise ratio values ​​of all radar channel signals corresponding to each target grid with the sum threshold of the signal-to-noise ratio value. Compare and compare the number of over-threshold channels corresponding to each target grid with the channel number threshold Compare and filter the corresponding signal-to-noise ratio values ​​to be greater than or equal to the signal-to-noise ratio value sum threshold And the corresponding number of channels that pass the threshold is greater than or equal to the channel number threshold The target grid.

[0110] In other words, step S163 can be used to set the target grid array The sum of the signal-to-noise ratio values ​​of all radar channel signals corresponding to the i-th target grid Sum threshold of signal-to-noise ratio Compare and calculate the number of over-threshold channels corresponding to the i-th target grid and channel number threshold Compare and filter and The target grid.

[0111] Step S164: If the screening result is empty, the target set The final grid information in is taken as the real target information. That is, if step S163 does not filter out the and , that is, there is no target grid that satisfies and The target grid, then the target set The grid information in is the real target information, and the mirror target elimination is completed. and The target grid of and If the target grid is not found, the process returns to step S130.

[0112] Compared with the prior art, the mirror elimination method provided by the embodiment of the present disclosure provides a screening criterion for eliminating mirror targets, namely the target threat degree, which can effectively improve the accuracy of mirror elimination and enable the distributed collaborative detection system to obtain better detection efficiency.

[0113] Another embodiment of the present disclosure relates to an image elimination device, such as Figure 5As shown, it includes a virtual node construction module 510, a registration module 520, a distance function construction module 530, a deflection angle function construction module 540, a threat assessment function construction module 550, and a selection module 560.

[0114] The virtual node construction module 510 is used to construct the virtual node center position corresponding to the distributed collaborative detection system according to the distribution of each sensor node in the distributed collaborative detection system.

[0115] The registration module 520 is used to read the echo data of each radar channel of the distributed collaborative detection system, divide the common view area space corresponding to the echo data of each radar channel into multiple spatial grids, align each radar channel signal with each spatial grid based on the echo data of each radar channel, and determine the target grid array according to the registration results.

[0116] The distance function construction module 530 is used to construct a comprehensive virtual node distance function based on the relationship between the positions of target grids with the same signal-to-noise ratio value in the target grid array and the center positions of the virtual nodes.

[0117] The deflection angle function construction module 540 is used to construct a comprehensive virtual array face pointing deviation angle function based on the deviation angles between the target grids with the same signal-to-noise ratio value in the target grid array and the array face pointing of each sensor node;

[0118] The threat level assessment function construction module 550 is used to construct a grid target threat level assessment function using the integrated virtual node distance function and the integrated virtual array pointing deviation angle function.

[0119] The selection module 560 is used to use the grid target threat evaluation function to respectively evaluate the target threat levels of target grids with the same signal-to-noise ratio in the target grid array, and select the information corresponding to the target grid with the highest target threat level as the true target information.

[0120] The specific implementation method of the mirror image elimination device provided in the embodiment of the present disclosure can be found in the description of the mirror image elimination method provided in the embodiment of the present disclosure, and will not be repeated here.

[0121] Compared with the prior art, the mirror image elimination device provided in the embodiment of the present disclosure provides a screening standard, namely the target threat degree, for the elimination of mirrored targets, which can effectively improve the accuracy of mirror image elimination and enable the distributed collaborative detection system to obtain better detection efficiency.

[0122] Another embodiment of the present disclosure relates to an electronic device, such as Figure 6 As shown, including:

[0123] at least one processor 610; and,

[0124] A memory 620 in communication with at least one processor 610; wherein,

[0125] The memory 620 stores instructions that can be executed by the at least one processor 610. The instructions are executed by the at least one processor 610 so that the at least one processor 610 can perform the image elimination method described in the above embodiment.

[0126] The memory and processor are connected using a bus, which can include any number of interconnected buses and bridges. The bus connects various circuits of one or more processors and memories. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits. These are all well known in the art and are therefore not described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single component or multiple components, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over a wireless medium via an antenna. Furthermore, the antenna receives data and transmits it to the processor.

[0127] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

[0128] Another embodiment of the present disclosure relates to a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the image removal method described in the above embodiment is implemented.

[0129] That is, those skilled in the art will understand that all or part of the steps in the methods described in the above embodiments can be implemented by instructing related hardware through a program. The program is stored in a storage medium and includes a number of instructions for causing a device (such as a microcontroller or chip) or a processor to execute all or part of the steps in the methods described in the various embodiments of the present disclosure. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0130] Those skilled in the art will appreciate that the above-mentioned embodiments are specific embodiments for implementing the present disclosure, and that in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present disclosure.

Claims

1. A method for eliminating mirror images, characterized in that: The mirror image elimination method comprises: According to the distribution of each sensor node in the distributed collaborative detection system, a virtual node center position corresponding to the distributed collaborative detection system is constructed; Reading echo data of each radar channel of the distributed collaborative detection system, dividing the common view area space corresponding to the echo data of each radar channel into a plurality of spatial grids, registering each radar channel signal with each of the spatial grids based on the echo data of each radar channel, and determining a target grid array according to the registration result; Constructing a comprehensive virtual node distance function based on the relationship between the position of the target grid with the same signal-to-noise ratio value in the target grid array and the center position of the virtual node; Based on the deviation angles between the target grids with the same signal-to-noise ratio value in the target grid array and the array faces of each sensor node, a comprehensive virtual array face pointing deviation angle function is constructed; Using the comprehensive virtual node distance function and the comprehensive virtual array pointing deviation angle function, a grid target threat assessment function is constructed; The target threat degree of target grids with the same signal-to-noise ratio value in the target grid array is respectively evaluated using the grid target threat degree evaluation function, and information corresponding to the target grid with the highest target threat degree is selected as the real target information.

2. The image elimination method according to claim 1, characterized in that: The step of constructing a virtual node center position corresponding to the distributed collaborative detection system according to the distribution of each sensor node in the distributed collaborative detection system includes: Acquire location information of each of the sensor nodes, the location information including latitude, longitude, and altitude; According to the location information and the importance of each sensor node to the distributed collaborative detection system, the virtual node center position is determined, and the virtual node center position includes the latitude of the virtual node center ,longitude ,high Respectively expressed as: ； in, Respectively represent the latitudes of the 1st, 2nd,…,Nrth sensor nodes; Respectively represent the longitudes of the 1st, 2nd, …, Nrth sensor nodes; Respectively represent the heights of the 1st, 2nd, …, Nrth sensor nodes; They respectively represent the critical weights of the 1st, 2nd, ..., Nrth sensor nodes, and are used to indicate the importance of the 1st, 2nd, ..., Nrth sensor nodes to the distributed collaborative detection system; Nr represents the total number of sensor nodes in the distributed collaborative detection system.

3. The image elimination method according to claim 2, characterized in that: The method comprises: reading echo data of each radar channel of the distributed collaborative detection system, dividing the common view area space corresponding to the echo data of each radar channel into a plurality of spatial grids, registering each radar channel signal with each of the spatial grids based on the echo data of each radar channel, and determining a target grid array according to the registration result, including: Read the echo data of each radar channel and store it in Arrays; According to the preset longitude interval , latitude interval , height interval , dividing the common viewing area space into a plurality of the spatial grids; According to the azimuth, pitch angle, and distance corresponding to each of the spatial grids and each of the sensor nodes, the corresponding positions of the spatial grids and the sensor nodes are calculated respectively. The range unit in the array completes the registration of each radar channel signal with each spatial grid; According to the registration result, the signal-to-noise ratio value of each radar channel signal corresponding to each of the spatial grids is counted, and the sum of the signal-to-noise ratio values ​​of all radar channel signals corresponding to each of the spatial grids is calculated; wherein the signal-to-noise ratio value of the td-th radar channel signal corresponding to the i-th spatial grid is expressed as , the sum of the signal-to-noise ratio values ​​of all radar channel signals corresponding to the i-th spatial grid is expressed as ,and , N represents the total number of radar channel signals corresponding to the i-th spatial grid; The signal-to-noise ratio values ​​of the radar channel signals corresponding to the spatial grids are compared with the preset signal-to-noise ratio thresholds. Compare and count the number of over-threshold channels corresponding to each of the spatial grids; wherein the number of over-threshold channels corresponding to the spatial grid is used to indicate that the signal-to-noise ratio value in each radar channel signal corresponding to the spatial grid exceeds the signal-to-noise ratio value threshold The number of radar channel signals; the number of over-threshold channels corresponding to the i-th spatial grid is expressed as ; The sum of the signal-to-noise ratio values ​​of all radar channel signals corresponding to each of the spatial grids is compared with the preset signal-to-noise ratio value sum threshold. Compare and compare the number of over-threshold channels corresponding to each of the spatial grids with the preset channel number threshold Compare and select the corresponding signal-to-noise ratio value whose sum is greater than or equal to the signal-to-noise ratio value sum threshold And the corresponding number of over-threshold channels is greater than or equal to the channel number threshold The spatial grid is used as the target grid, and the target grid is stored in the target grid array ; Where, the target grid array The i-th element in Expressed as , i is the target grid array The element index of Represents the target raster arrays respectively The center longitude, center latitude, and center height of the spatial grid corresponding to the i-th element in , Represents the target raster arrays respectively The signal-to-noise ratio values ​​of the 1st, 2nd, …, Nth radar channel signals corresponding to the spatial grid corresponding to the i-th element in .

4. The image elimination method according to claim 3, characterized in that: The step of constructing a comprehensive virtual node distance function based on the relationship between the position of the target grid with the same signal-to-noise ratio value in the target grid array and the center position of the virtual node comprises: According to the sum of signal-to-noise ratio , for the target grid array Sort the elements in descending order; Select the target grid array The target grids with the same signal-to-noise ratio value as the first element are selected as candidate grids to form a candidate grid group; According to the following formula, the comprehensive virtual node distance function corresponding to the i-th candidate grid in the candidate grid group is constructed: : ； in, Indicates the maximum value of the distance between the position of each candidate grid in the candidate grid group and the center position of the virtual node.

5. The image elimination method according to claim 4, characterized in that: The step of constructing a comprehensive virtual array face pointing deviation angle function based on the deviation angles between the target grids with the same signal-to-noise ratio value in the target grid array and the array face pointing of each sensor node comprises: According to the following formula, the comprehensive virtual array pointing deviation angle function corresponding to the i-th candidate grid in the candidate grid group is constructed: : ； in, Respectively represent the deviation angles between the i-th candidate grid in the candidate grid group and the array direction of the 1st, 2nd, …, Nrth sensor nodes; It represents the maximum value of the deviation angle between each candidate grid in the candidate grid group and the array direction of each sensor node.

6. The image elimination method according to claim 5, characterized in that: The method of using the comprehensive virtual node distance function and the comprehensive virtual array pointing deviation angle function to construct a grid target threat assessment function includes: According to the following formula, the grid target threat evaluation function corresponding to the i-th candidate grid in the candidate grid group is constructed: : 。 7. The image elimination method according to claim 6, characterized in that: The method of using the grid target threat degree evaluation function to respectively evaluate the target threat degrees of target grids with the same signal-to-noise ratio value in the target grid array, and selecting information corresponding to the target grid with the highest target threat degree as the real target information, includes: Using the grid target threat assessment function , respectively evaluating the target threat degree of each candidate grid in the candidate grid group; Arrange the candidate grids in the candidate grid group in descending order according to the target threat level, select the candidate grid with the largest target threat level as the final grid and store it in the target set , and the corresponding The echo values ​​of each radar channel in the array are cleared to zero, and the target grid array is clearing the corresponding information of the final grid; according to Array recount target grid array The signal-to-noise ratio value of each radar channel signal corresponding to each of the target grids in and the sum of the signal-to-noise ratio values ​​of all radar channel signals; The signal-to-noise ratio values ​​of the radar channel signals corresponding to the target grids are respectively compared with the signal-to-noise ratio threshold Compare and count the number of over-threshold channels corresponding to each target grid; The sum of the signal-to-noise ratio values ​​of all radar channel signals corresponding to the target grids is respectively compared with the sum threshold of the signal-to-noise ratio value. Compare and compare the number of over-threshold channels corresponding to each target grid with the channel number threshold Compare and select the corresponding signal-to-noise ratio value whose sum is greater than or equal to the signal-to-noise ratio value sum threshold And the corresponding number of over-threshold channels is greater than or equal to the channel number threshold If the result of the filter is empty, the target grid will be The information of the final grid in is used as the real target information.

8. An image elimination device, characterized in that: The image elimination device comprises: A virtual node construction module, used to construct a virtual node center position corresponding to the distributed collaborative detection system according to the distribution of each sensor node in the distributed collaborative detection system; A registration module, used for reading the echo data of each radar channel of the distributed collaborative detection system, dividing the common view area space corresponding to the echo data of each radar channel into a plurality of spatial grids, registering each radar channel signal with each of the spatial grids based on the echo data of each radar channel, and determining a target grid array according to the registration result; A distance function construction module, used for constructing a comprehensive virtual node distance function based on the relationship between the position of the target grid with the same signal-to-noise ratio value in the target grid array and the center position of the virtual node; A deviation angle function construction module, used to construct a comprehensive virtual array face pointing deviation angle function based on the deviation angles between the target grids with the same signal-to-noise ratio value in the target grid array and the array face pointing of each sensor node; A threat assessment function construction module is used to construct a grid target threat assessment function using the comprehensive virtual node distance function and the comprehensive virtual array pointing deviation angle function; The selection module is used to use the grid target threat degree evaluation function to respectively evaluate the target threat degrees of target grids with the same signal-to-noise ratio value in the target grid array, and select the information corresponding to the target grid with the highest target threat degree as the real target information.

9. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the image elimination method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the image elimination method according to any one of claims 1 to 7 is implemented.

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