An echo correlation method based on a threshold-crossing sparse spatial grid

Abstract

The present invention discloses an echo correlation method based on a threshold-crossing sparse spatial grid, which includes: each node radar performs collaborative detection on the area to be detected to obtain data that needs to be input into the fusion center; all effective threshold-crossing sparse spatial grids are obtained according to the spatial geometric information; the spatial grids are correlated through the spatial position relationship, and the effective spatial grids belonging to the same area are classified to obtain a second finite set storing the correlated spatial grid information; based on the correlated spatial grid information, a grid fusion center is obtained through a fusion algorithm, and then the echoes of the transceiver-separated channels are correlated to obtain a third finite set storing the correlated echo data; the redundant echoes associated with the threshold-crossing sparse spatial grids corresponding to the false targets in the third finite set are eliminated, and thus the correlation of the echo data is completed. This method reduces the number of spatial grids, simplifies the determination criterion of the spatial grid size, and reduces the processing pressure on the fusion center.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar, and particularly relates to an echo correlation method based on a sparse space grid across thresholds, which is applicable to the spatial registration work before target detection in a multi-missile distributed cooperative detection system. Background Art

[0002] Currently, with the development of information fusion technology, the target detection technology in the field of multi-missile cooperative detection has also been continuously developed and evolved. However, most of the current research on signal-level fusion detection problems is based on a prerequisite, that is, to determine whether a target exists in the distance cells of interest, and then switch detection cells one by one to complete the target detection work for the entire airspace of interest. It can be seen that the focus of related research is on how to use algorithms to improve the detection performance of signal-level fusion. However, from the perspective of engineering applications, for a multi-missile cooperative detection system, a specific target is located in different distance cells in different channels of the entire system. Therefore, for the same target, the azimuths relative to different transmitting stations and receiving stations are different, and the transceiver distances in different channels are different, resulting in the asynchronization of the signal space, so that a specific target will fall on different distance cells in different channels. These different distance cells overlap with each other, and the overlapping regions are irregular, so it is impossible to directly determine the detection cells for detection.

[0003] Generally, the process of determining the detection cells corresponding to the same target in different channels is called spatial registration or echo correlation. The spatial registration problem can be divided into two stages according to the presence or absence of target prior information: one is the spatial registration during target search, when there is no target prior information, and the difficulty of spatial registration is relatively large; the other is the spatial registration during target tracking, when there is already a certain amount of target prior information, and the difficulty of spatial registration is relatively easy. The existing spatial registration technologies during target tracking mainly include the technology of traversing distance cells and the registration technology based on spatial grids. They can all accurately complete the above task of determining detection cells, but they all have the problems of high algorithm complexity and large data volume, and are difficult to implement in actual engineering applications. Summary of the Invention

[0004] In order to solve the above problems existing in the prior art, the present invention provides an echo correlation method based on a sparse space grid across thresholds. The technical problems to be solved by the present invention are realized through the following technical solutions:

[0005] An echo correlation method based on a sparse space grid across thresholds, the echo correlation method comprising:

[0006] S1. Each node radar performs cooperative detection on the area to be detected to obtain the data to be input to the fusion center, where the data to be input to the fusion center includes the upper and lower bounds of the distance cells where the target exists and the signal to be detected;

[0007] S2, the fusion center receives data from the node radar that needs to be input into the fusion center, and obtains all valid sparse spatial grids that pass the threshold according to the spatial geometric information, wherein all valid sparse spatial grids that pass the threshold constitute a first finite set;

[0008] S3, associating the valid over-threshold sparse spatial grids by spatial position relationship, classifying the valid over-threshold sparse spatial grids belonging to the same area, and obtaining a second finite set of spatial grid information storing association;

[0009] S4, based on the associated spatial grid information, a grid fusion center is obtained through a fusion algorithm, and then the echoes of the transmitting and receiving channels are associated to obtain a third finite set storing associated echo data;

[0010] S5. Utilize a false target removal algorithm to eliminate redundant echoes that are associated with the over-threshold sparse spatial grid corresponding to the false targets in the third finite set, thereby completing the association of the echo data.

[0011] In one embodiment of the present invention, step S1 comprises:

[0012] Each node radar performs collaborative detection on the area to be detected, and the received echo signals are subjected to frequency conversion demodulation and matched filter preprocessing in sequence, and then processed by local limitation and one-dimensional range image in sequence to obtain the upper and lower bounds of the range unit where the target exists, so as to obtain the data that needs to be input into the fusion center according to the upper and lower bounds of the range unit where the target exists and the signal to be detected.

[0013] In one embodiment of the present invention, in the absence of observation errors, step S2 includes:

[0014] S2.11. The fusion center performs a full combination of N node radars to obtain u minimum operation units, and the u minimum operation units constitute a minimum operation unit group;

[0015] S2.12. Randomly select a minimum operation unit from the minimum operation unit group;

[0016] S2.13, using spatial geometric information to obtain all intersecting sparse spatial grids that pass the threshold in the current minimum operation unit;

[0017] S2.14, determining whether the sparse spatial grid that exceeds the threshold is located in the common view area of ​​the current minimum operation unit, and removing the sparse spatial grid that exceeds the threshold and is not in the common view area from the first finite set;

[0018] S2.15, putting the remaining sparse spatial grid data that exceeds the threshold into the first finite set;

[0019] S2.16. Delete the current minimum operation unit from the minimum operation unit group, and determine whether the minimum operation unit group is empty. If it is not empty, jump to S2.12 until the minimum operation unit group is empty, and all valid sparse spatial grids passing the threshold are obtained.

[0020] In an embodiment of the present invention, in the case of the existence of observation errors, step S2 includes:

[0021] Step S2.21. Combine the single-station channel of the node radar with the transceiver-separated channel to obtain N groups of data sets, and the N groups of data sets form a data set group;

[0022] Step S2.22. Randomly select a certain data set from the data set group;

[0023] Step S2.23. Use the spatial geometric information to obtain all the intersecting sparse spatial grids passing the threshold in the minimum operation unit of the current data set;

[0024] Step S2.24. Determine whether the sparse spatial grids passing the threshold are located in the common visibility area of the current minimum operation unit, and eliminate the sparse spatial grids passing the threshold that are not in the common visibility area from the N groups of data sets;

[0025] Step S2.25. Put the remaining sparse spatial grid data passing the threshold in the current data set into a finite set;

[0026] Step S2.26. Delete the current data set from the data set group, and determine whether the data set group is empty. If it is not empty, jump to S2.22 until the data set group is empty, and all valid sparse spatial grids passing the threshold are obtained.

[0027] In an embodiment of the present invention, step S3 includes:

[0028] S3.1. Create an initial second finite set;

[0029] S3.2. Randomly select a certain sparse spatial grid passing the threshold in the first finite set, delete it from the first finite set, and store it in the initial second finite set;

[0030] S3.3. Sequentially take out the remaining sparse spatial grids passing the threshold in the first finite set;

[0031] S3.4. Randomly select the first spatial grid of the sparse spatial grid passing the threshold from the second finite set storing the sparse spatial grid passing the threshold, and use this spatial grid as the reference spatial grid for subsequent comparison;

[0032] S3.5. Compare the remaining spatial grids of the over-threshold sparse spatial grids in the first finite set with the reference spatial grid one by one to obtain all the spatial grids whose spatial positions overlap with the reference spatial grid;

[0033] S3.6. Select the spatial grids obtained in step S3.5, compare the straight-line distance between the centroid of the spatial grid and the centroid of the reference spatial grid, and associate the spatial grid with the shortest straight-line distance with the reference spatial grid to obtain the associated spatial grid;

[0034] S3.7. Store the associated spatial grids in the second finite set, and they belong to the same over-threshold sparse spatial grid as the corresponding reference spatial grid;

[0035] S3.8. Delete the associated spatial grids from the first finite set in step S3.3;

[0036] S3.9. Determine whether there is a reference spatial grid in the second finite set that has not been compared with the spatial grids of the remaining over-threshold sparse spatial grids in the first finite set. If so, jump to step S3.4. Otherwise, sequentially store the remaining spatial grids in the selected over-threshold sparse spatial grids in step S3.3 into the blank positions in the second finite set as new reference spatial grids, and delete the selected over-threshold sparse spatial grids from the first finite set;

[0037] S3.10. Determine whether the first finite set is empty. If so, obtain the second finite set storing the information of the associated spatial grids. If not, jump to step S3.3 until the first finite set is empty.

[0038] In an embodiment of the present invention, step S4 includes:

[0039] S4.1. Create an initial third finite set;

[0040] S4.2. Randomly select a certain associated spatial grid in the second finite set;

[0041] S4.3. Obtain the centroids of all the spatial grids in the associated spatial grid selected in step S4.2, and use the weighted centroid localization algorithm to obtain the fusion center;

[0042] S4.4. Obtain the echo data of all the transceiver-displaced channels, store them in the fourth finite set, and use the spatial geometric principle to obtain the elliptical ring region where the target exists. If the fusion center is located within the elliptical ring region, then associate the fusion center with the echo data, and then store the associated echo data in the third finite set as one of its elements;

[0043] S4.5. Delete the associated echo data from the fourth finite set, and at the same time, remove the associated spatial grids in the second finite set selected in step S4.2, and determine whether the third finite set is empty at this time. If it is not empty, jump to step S4.2. Otherwise, synchronously update the third finite set to obtain the third finite set storing the associated echo data.

[0044] In an embodiment of the present invention, step S5 includes:

[0045] S5.1. Randomly select a set of echo data from the third finite set obtained in step S4;

[0046] S5.2. Randomly select the remaining echo data in the third finite set obtained in step S4, and compare it with the echo data selected in step S5.1 to determine whether there are two sets of exactly the same echo data. If so, retain the set of echo data with the larger cluster, delete the other set of echo data, and at the same time update the number of echo data in the third finite set;

[0047] S5.3. Determine whether there is echo data in the third finite set that has not been compared with the echo data selected in step S5.1. If so, jump to step S5.2. If not, execute step S5.4;

[0048] S5.4. Determine whether the number of echo data of the echo data selected in step S5.2 is 0. If it is not 0, place this set of echo data in a new fifth finite set;

[0049] S5.5. Delete the echo data selected in step S5.2 from the third finite set, and determine whether the third finite set is empty. If it is not empty, jump to step S5.1 until the third finite set is empty, and complete the association of the echo data.

[0050] Advantages of the present invention:

[0051] 1. Under the algorithm framework of dual-threshold signal-level fusion detection, the present invention screens out the range of distance units where the target exists through a relatively high local threshold and one-dimensional range profile processing and sends it to the fusion center. Then, by reasonably allocating the radar group, the sparse spatial grids after crossing the threshold are determined using the principle of spatial geometry. Through the association of spatial grids and related registration processing, the association of echoes is completed. At the same time, the method is further improved in the case of observation errors, the formation rule of the spatial grid is changed, and the spatial grids are associated by the density-based clustering algorithm, so as to achieve the purpose of echo association. Compared with the prior art, this method reduces the number of spatial grids, simplifies the determination criterion of the spatial grid size, reduces the processing pressure on the fusion center, and is more suitable for practical engineering.

[0052] 2. An adaptive maximum spatial grid is obtained through local threshold and one-dimensional range profile processing. Compared with the prior art method of first setting a partitioning criterion, then performing grid partitioning, and finally comparing the measured values of range cells, the steps are greatly simplified.

[0053] 3. Compared with the prior art, the number of spatial grids is greatly reduced, and at the same time, the amount of data communicated with the fusion center is also significantly reduced, improving the association efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a schematic flowchart of an echo association method based on a sparse spatial grid beyond a threshold provided by an embodiment of the present invention;

[0055] Figure 2 is a flowchart of step S2 of the present invention;

[0056] Figure 3 is an improved flowchart of step S2 of the present invention;

[0057] Figure 4 is a flowchart of step S3 of the present invention;

[0058] Figure 5 is an improved flowchart of step S3 of the present invention;

[0059] Figure 6 is a flowchart of step S4 of the present invention;

[0060] Figure 7 is an improved flowchart of step S4 of the present invention;

[0061] Figure 8 is a flowchart of step S5 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0062] The present invention will be further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0063] Embodiment 1

[0064] The main idea of the present invention: Based on the existing spatial grid registration technology, combined with the radar positioning idea, using the local threshold of the dual-threshold signal-level fusion detector and the spatial geometric principle, a sparse spatial grid beyond the threshold is obtained, and the echo association is completed by associating the spatial grids to achieve the purpose of spatial registration.

[0065] Based on the above idea, please refer to Figure 1 , Figure 1 is a schematic flowchart of an echo association method based on a sparse spatial grid beyond a threshold provided by an embodiment of the present invention. The present invention proposes an echo association method based on a sparse spatial grid beyond a threshold, and the echo association method includes:

[0066] S1. Each node radar collaboratively detects the area to be detected to obtain the data to be input into the fusion center. The data to be input into the fusion center includes the upper and lower bounds of the range cells where the target exists and the signal to be detected.

[0067] Specifically, each node radar collaboratively detects the area to be detected. After the received echo signal is successively subjected to frequency conversion demodulation and preprocessing by a matched filter, the upper and lower bounds of the range cells where the target exists are obtained through local thresholding and one-dimensional range profile processing in sequence, so as to obtain the data to be input into the fusion center based on the upper and lower bounds of the range cells where the target exists and the signal to be detected. Among them, the local thresholding is used to limit the signal within the required range.

[0068] S2. The fusion center receives the data to be input into the fusion center transmitted by the node radars, and obtains all valid sparse spatial grids that cross the threshold according to the spatial geometric information. All valid sparse spatial grids that cross the threshold form a first finite set, and the first finite set is denoted as U = {U1, U2,..., U i ,..., U u}, where u is the number of minimum operation units.

[0069] In a specific embodiment, in the case of no observation error, please refer to Figure 2 . Step S2 includes:

[0070] S2.11. The fusion center performs a full combination of N node radars to obtain u minimum operation units. The u minimum operation units form a minimum operation unit group, where

[0071] S2.12. Randomly select a certain minimum operation unit from the minimum operation unit group.

[0072] S2.13. Use the spatial geometric information to obtain all the sparse spatial grids that cross the threshold and intersect within the current minimum operation unit.

[0073] Specifically, with the coordinates of the node radar as the center and all the upper and lower bounds of the range cells transmitted from the node radar to the fusion center as the radii, draw all the circles. Each pair of upper and lower bounds forms a circular ring, so as to determine that the target is within this circular ring range. Therefore, all the sparse spatial grids that cross the threshold and intersect within the minimum operation unit can be obtained.

[0074] S2.14. Determine whether the sparse spatial grids that cross the threshold are located within the common visibility area of the current minimum operation unit, and remove the sparse spatial grids that cross the threshold and are not within the common visibility area from the first finite set;

[0075] S2.15, put the remaining sparse spatial grid data that exceeds the threshold into the first finite set U = {U1, U2, ..., U i ,...,U u}, u is the number of minimum operation units, where U i ={D i1 ,D i2 ,...,D ij ,...,D it} is the total spatial grid data of each minimum operation unit, and t is the number of spatial grids in the minimum operation unit within the common view area. is the data set corresponding to a single spatial grid, m = 1, 2, 3, 4 is the spatial grid vertex number, n = 1, 2 is the spatial grid channel number. Where I is the number of the spatial grid, are the coordinates of the four vertices of the space grid, (x a ,y a ) is the coordinate of the centroid of the spatial grid corresponding to D, L is the number of echoes associated with the spatial grid (the initial value is 1), SNR n is the signal-to-noise ratio of the two channels corresponding to the spatial grid, F is the valid flag of the spatial grid, O n is the serial number of the self-transmitting and self-receiving channel corresponding to the spatial grid, P n It is the sequence number of the echo corresponding to the spatial grid in each channel.

[0076] S2.16, delete the current minimum operation unit from the minimum operation unit group, determine whether the minimum operation unit group is empty, if not, jump to S2.12, until the minimum operation unit group is empty, and obtain all valid over-threshold sparse spatial grids.

[0077] In a specific embodiment, in the presence of observation errors, see Figure 3 , step S2 comprises:

[0078] Step S2.21, combining the node radar single station channel and the transmit-receive split channel to obtain N sets of data sets, and the N sets of data sets constitute a data set group.

[0079] Step S2.22: Randomly select a data set from the data set group.

[0080] Step S2.23, using the spatial geometric information to obtain all intersecting sparse spatial grids that pass the threshold in the minimum operation unit of the current data set. The specific process of this step is similar to step S2.13 and will not be repeated here.

[0081] Step S2.24: determine whether the sparse spatial grids that exceed the threshold are located in the common view area of ​​the current minimum operation unit, and remove the sparse spatial grids that exceed the threshold and are not in the common view area from the N sets of data sets.

[0082] Step S2.25: Put the remaining sparse spatial grid data exceeding the threshold in the current dataset into the first finite set;

[0083] Step S2.26: Delete the current dataset from the dataset group, check if the dataset group is empty. If not, jump to S2.22 until the dataset group is empty, obtaining all valid sparse spatial grids exceeding the threshold.

[0084] S3: Correlate the valid sparse spatial grids exceeding the threshold through their spatial position relationships, classify the valid sparse spatial grids exceeding the threshold belonging to the same region, and obtain a second finite set storing the correlated spatial grid information.

[0085] In a specific embodiment, in the absence of observation errors, please refer to Figure 4 , step S3 includes:

[0086] S3.1: Create an initial second finite set Z, which is used to store all the information of the correlated spatial grid groups.

[0087] S3.2: Randomly select a sparse spatial grid exceeding the threshold in the first finite set U, delete it from the first finite set U, and store it in the initial second finite set Z.

[0088] Specifically, each spatial grid of the sparse spatial grid exceeding the threshold can be stored in the second finite set Z column by column.

[0089] S3.3: Sequentially take out the remaining sparse spatial grids exceeding the threshold in the first finite set U.

[0090] S3.4: Randomly select the first spatial grid of the sparse spatial grid exceeding the threshold stored in the second finite set Z, and use this spatial grid as the reference spatial grid for subsequent comparison.

[0091] S3.5: Compare the spatial grids of the remaining sparse spatial grids exceeding the threshold in the first finite set U with the reference spatial grid one by one, and obtain all the spatial grids whose spatial positions overlap with the reference spatial grid.

[0092] S3.6: Select the spatial grids obtained in step S3.5, compare the straight-line distance between the centroid of the spatial grid and the centroid of the reference spatial grid, and correlate the spatial grid with the shortest straight-line distance with the reference spatial grid to obtain the correlated spatial grid.

[0093] S3.7: Store the correlated spatial grids in the second finite set Z, and the correlated spatial grids and the corresponding reference spatial grids belong to the same sparse spatial grid exceeding the threshold, that is, the correlated spatial grids and the corresponding reference spatial grids are in the same column of the second finite set Z.

[0094] S3.8. Delete the associated spatial grids from the first finite set U in step S3.3.

[0095] S3.9. Determine whether there is a reference spatial grid in the second finite set Z that has not been compared with the remaining over-threshold sparse spatial grids in the first finite set U. If so, jump to step S3.4. Otherwise, sequentially store the remaining spatial grids among the selected over-threshold sparse spatial grids in the blank positions in the second finite set Z as new reference spatial grids, and delete the selected over-threshold sparse spatial grids from the first finite set U.

[0096] S3.10. Determine whether the first finite set U is empty. If so, obtain the second finite set Z storing the associated spatial grid information. If not, jump to step S3.3 until the first finite set U is empty.

[0097] In a specific embodiment, in the case of the existence of observation errors, please refer to Figure 5 , improve step S3, which may include the following steps:

[0098] (3a) Create a finite set Z;

[0099] (3b) Create a sample set D with the centroid coordinates of the effective over-threshold sparse spatial grids obtained in step 2 as samples;

[0100] (3c) Set the neighborhood parameters;

[0101] (3d) Select the Lp distance as the sample distance metric;

[0102] (3e) Use the DBSCAN algorithm to obtain all sample cluster partitions and store them in Z.

[0103] S4. Based on the associated spatial grid information, obtain a grid fusion center through a fusion algorithm, and then perform association on the echoes of the bistatic channels to obtain a third finite set storing the associated echo data.

[0104] In a specific embodiment, in the case of the non-existence of observation errors, please refer to Figure 6 , step S4 includes:

[0105] S4.1. Create an initial third finite set V for storing the associated echo data.

[0106] S4.2. Randomly select a certain associated spatial grid in the second finite set Z.

[0107] S4.3. Obtain the centroid of all spatial grids in the associated spatial grid selected in step S4.2, and obtain the fusion center using a weighted centroid positioning algorithm.

[0108] S4.4. Obtain the echo data of all the transmitting and receiving channels and store them in the fourth finite set. Use the principles of spatial geometry to obtain the elliptical ring area where the target exists. If the fusion center is located in the elliptical ring area, associate the fusion with the echo data, and then store the associated echo data in the third finite set V as one of its elements.

[0109] S4.5. Delete the associated echo data from the fourth finite set, and at the same time eliminate the associated spatial grids in the second finite set Z selected in step S4.2, and determine whether the third finite set V is empty at this time. If not, jump to step S4.2, otherwise synchronously update the third finite set V to obtain the third finite set V that stores the associated echo data.

[0110] In a specific embodiment, in the presence of observation errors, see Figure 7 , step S4 can be improved to include the following steps:

[0111] Store the corresponding echo information in Z into V.

[0112] S5. Utilize a false target removal algorithm to eliminate redundant echoes that are associated with the over-threshold sparse spatial grid corresponding to the false targets in the third finite set, thereby completing the association of the echo data.

[0113] In a specific embodiment, see Figure 8 , step S5 comprises:

[0114] S5.1. Randomly select a group of echo data from the third finite set V obtained in step S4;

[0115] S5.2. Randomly select the remaining echo data in the third finite set V obtained in step S4, and compare them with the echo data selected in step S5.1 to determine whether there are two completely identical groups of echo data. If so, retain the group of echo data with the larger cluster, delete the other group of echo data, and update the number of echoes in the third finite set V at the same time.

[0116] S5.3. Determine whether there is echo data in the third finite set V that has not been compared with the echo data selected in step S5.1. If so, jump to step S5.2; if not, execute step S5.4.

[0117] S5.4, determining whether the number of echoes of the echo data selected in step S5.2 is 0, if not 0, placing the group of echo data into a new fifth finite set X;

[0118] S5.5. Delete the echo data selected in step S5.2 from the third finite set V, and determine whether the third finite set V is empty. If it is not empty, jump to step S5.1 until the third finite set V is empty, and the association of the echo data is completed.

[0119] The present invention proposes an echo association method based on a threshold-crossing sparse spatial grid. This echo association method based on a threshold-crossing sparse spatial grid can reduce the number of spatial grid divisions, simplify the method for determining the size of the spatial grid, reduce the complexity of the existing spatial registration technology, thereby reducing the processing pressure on the fusion center and improving the efficiency of spatial registration and target detection.

[0120] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification.

[0121] Although the present application has been described in conjunction with various embodiments herein, however, in the process of implementing the claimed present application, those skilled in the art can understand and achieve other variations of the disclosed embodiments by viewing the accompanying drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "one" does not exclude a plurality of situations. A single processor or other unit can implement several functions recited in the claims. Certain measures are recited in mutually different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0122] The above content is a further detailed description of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention pertains, without departing from the concept of the present invention, several simple deductions or substitutions can still be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. An echo correlation method based on a sparse spatial grid with a threshold crossing, characterized in that, The echo association method comprises: S1. Each node radar performs collaborative detection on the area to be detected to obtain data that needs to be input into the fusion center. The data that needs to be input into the fusion center includes the upper and lower bounds of the distance unit where the target exists and the signal to be detected; S2, the fusion center receives data from the node radar that needs to be input into the fusion center, and obtains all valid sparse spatial grids that pass the threshold according to the spatial geometric information, wherein all valid sparse spatial grids that pass the threshold constitute a first finite set; S3, associating the valid over-threshold sparse spatial grids by spatial position relationship, classifying the valid over-threshold sparse spatial grids belonging to the same area, and obtaining a second finite set of spatial grid information storing association; S4, based on the associated spatial grid information, a grid fusion center is obtained through a fusion algorithm, and then the echoes of the transmitting and receiving channels are associated to obtain a third finite set storing associated echo data; S5. Utilize a false target removal algorithm to eliminate redundant echoes that are associated with the over-threshold sparse spatial grid corresponding to the false targets in the third finite set, thereby completing the association of the echo data.

2. The echo correlation method based on a sparse spatial grid with a threshold crossing as claimed in claim 1, wherein Step S1 includes: Each node radar performs collaborative detection on the area to be detected, and the received echo signals are subjected to frequency conversion demodulation and matched filter preprocessing in sequence, and then processed by local limitation and one-dimensional range image in sequence to obtain the upper and lower bounds of the range unit where the target exists, so as to obtain the data that needs to be input into the fusion center according to the upper and lower bounds of the range unit where the target exists and the signal to be detected.

3. The echo correlation method based on a threshold-crossing sparse spatial grid according to claim 1, wherein In the absence of observation errors, step S2 includes: S2.

11. The fusion center performs a full combination of N node radars to obtain u minimum operation units, and the u minimum operation units constitute a minimum operation unit group; S2.

12. Randomly select a minimum operation unit from the minimum operation unit group; S2.13, using spatial geometric information to obtain all intersecting sparse spatial grids that pass the threshold in the current minimum operation unit; S2.14, determining whether the sparse spatial grid that exceeds the threshold is located in the common view area of ​​the current minimum operation unit, and removing the sparse spatial grid that exceeds the threshold and is not in the common view area from the first finite set; S2.15, putting the remaining sparse spatial grid data that exceeds the threshold into the first finite set; S2.16, delete the current minimum operation unit from the minimum operation unit group, determine whether the minimum operation unit group is empty, if not, jump to S2.12, until the minimum operation unit group is empty, and obtain all valid over-threshold sparse spatial grids.

4. The echo correlation method based on a threshold-crossing sparse spatial grid according to claim 1, wherein In the case of observation errors, step S2 includes: Step S2.21, combining the node radar single station channel and the transmit-receive split channel to obtain N sets of data sets, wherein the N sets of data sets constitute a data set group; Step S2.22, randomly selecting a data set from the data set group; Step S2.23, using the spatial geometric information to obtain all intersecting sparse spatial grids that pass the threshold in the minimum operation unit of the current data set; Step S2.24: Determine whether the sparse spatial grid exceeding the threshold is located within the common view area of the current minimum operation unit, and remove the sparse spatial grids exceeding the threshold that are not within the common view area from the N groups of data sets; Step S2.25: Put the remaining sparse spatial grid data exceeding the threshold in the current data set into a finite set; Step S2.26: Delete the current data set from the data set group, determine whether the data set group is empty. If it is not empty, jump to S2.22 until the data set group is empty to obtain all valid sparse spatial grids exceeding the threshold.

5. The echo correlation method based on a threshold-crossing sparse spatial grid according to claim 1, wherein Step S3 includes: S3.1: Create an initial second finite set; S3.2: Randomly select a sparse spatial grid exceeding the threshold in the first finite set, delete it from the first finite set, and store it in the initial second finite set; S3.3: Sequentially take out the remaining sparse spatial grids exceeding the threshold in the first finite set; S3.4: Randomly select the first spatial grid of the sparse spatial grid exceeding the threshold stored in the second finite set, and use this spatial grid as the reference spatial grid for subsequent comparison; S3.5: Compare the spatial grids of the remaining sparse spatial grids exceeding the threshold in the first finite set with the reference spatial grid in sequence to obtain all the spatial grids whose spatial positions overlap with the reference spatial grid; S3.6: Select the spatial grids obtained in step S3.5, compare the straight-line distance between the centroid of the spatial grid and the centroid of the reference spatial grid, and associate the spatial grid with the shortest straight-line distance with the reference spatial grid to obtain the associated spatial grids; S3.7: Store the associated spatial grids in the second finite set, and they belong to the same sparse spatial grid exceeding the threshold as the corresponding reference spatial grid; S3.8: Delete the associated spatial grids from the first finite set in step S3.3; S3.9: Determine whether there is a reference spatial grid in the second finite set that has not been compared with the spatial grids of the remaining sparse spatial grids exceeding the threshold in the first finite set. If it exists, jump to step S3.

4. Otherwise, sequentially store the remaining spatial grids of the sparse spatial grids exceeding the threshold selected in step S3.3 in the blank positions in the second finite set as new reference spatial grids, and delete the selected sparse spatial grids exceeding the threshold from the first finite set; S3.10: Determine whether the first finite set is empty. If it is, obtain the second finite set storing the information of the associated spatial grids. If not, jump to step S3.3 until the first finite set is empty.

6. The echo correlation method based on a threshold-crossing sparse spatial grid according to claim 1, wherein Step S4 includes: S4.1: Create an initial third finite set; S4.2: Randomly select an associated spatial grid in the second finite set; S4.3: Obtain the centroids of all the spatial grids in the associated spatial grid selected in step S4.2, and use the weighted centroid localization algorithm to obtain the fusion center; S4.4, obtaining echo data of all transmitting and receiving channels, and storing them in the fourth finite set, using the principle of space geometry to obtain the elliptical ring area where the target exists, if the fusion center is located in the elliptical ring area, then correlating the fusion with the echo data, and then storing the correlated echo data in the third finite set as one of the elements; S4.

5. Delete the associated echo data from the fourth finite set, and at the same time remove the associated spatial grids in the second finite set selected in step S4.2, and determine whether the third finite set is empty at this time. If not, jump to step S4.2, otherwise synchronously update the third finite set to obtain the third finite set storing the associated echo data.

7. The echo correlation method based on a threshold-crossing sparse spatial grid according to claim 1, wherein Step S5 includes: S5.

1. Randomly select a group of echo data from the third finite set obtained in step S4; S5.2, randomly select the remaining echo data in the third finite set obtained in step S4, and compare them with the echo data selected in step S5.1 to determine whether there are two groups of echo data that are completely identical. If so, retain the group of echo data with a larger cluster, delete the other group of echo data, and update the number of echoes in the third finite set; S5.3, determine whether there is echo data in the third finite set that has not been compared with the echo data selected in step S5.1, if yes, jump to step S5.2, if not, execute step S5.4; S5.4, determining whether the number of echoes of the echo data selected in step S5.2 is 0, if not 0, placing the group of echo data into a new fifth finite set; S5.5, delete the echo data selected in step S5.2 from the third finite set, and determine whether the third finite set is empty. If not, jump to step S5.1 until the third finite set is empty, thereby completing the association of the echo data.

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