A passive multi-station multi-target cross location method for removing false points

Abstract

This invention relates to a passive multi-station multi-target cross-localization method for removing false points, comprising: determining the direction finding line corresponding to each direction finding station based on the angle of arrival information of each target obtained by each direction finding station; obtaining the intersection set of the direction finding lines of all direction finding stations using bi-station cross-localization; determining high-density points in the intersection set using a high-density point extraction algorithm; clustering the high-density points in the high-density point set into clusters based on the cutoff radius corresponding to all high-density points in the high-density point set; calculating the azimuth angle of the cluster center of each cluster relative to each direction finding station; filtering and merging the clusters in the cluster set based on the calculation results to determine the final cluster set; and obtaining the target localization result based on the average position of each cluster in the final cluster set. This invention is simple and effective, and can complete the target localization using only the angle information measured by multiple stations and its own position.

Description

Technical Field

[0001] This invention belongs to the field of electronic countermeasures technology, specifically relating to a passive multi-station multi-target cross-location method for removing false points. Background Technology

[0002] The role and importance of electronic warfare in modern warfare are constantly increasing, and radiation source localization is an important issue in electronic warfare. Although active positioning technology has the advantages of all-weather operation and high precision, it is also easily detected by the enemy, making it vulnerable to soft-kill attacks from enemy electronic jamming or hard-kill attacks from weapons such as anti-radiation missiles. This can greatly affect positioning accuracy and even threaten the security of the positioning system itself.

[0003] Passive positioning is a type of passive detection and reconnaissance positioning. It does not emit signals itself, but only receives signals from external radiation, thus possessing extremely strong stealth capabilities. It can obtain relevant parameters for calculating target position information from the received signals, thereby achieving target localization. Among passive positioning methods, direction finding cross-positioning is widely used due to its simple equipment, reliable azimuth information, long detection range, and high system sensitivity. However, in multi-target positioning scenarios, when multiple sensors use the azimuth angle measurements of multiple targets for cross-positioning, a large number of false points are generated. Summary of the Invention

[0004] To address the aforementioned problems in the existing technology, this invention provides a passive multi-station multi-target cross-localization method for removing false points. The technical problem to be solved by this invention is achieved through the following technical solution:

[0005] This invention provides a passive multi-station multi-target cross-localization method for removing false points, comprising:

[0006] Step 1: Based on the angle of arrival information of each target obtained by each direction finding station, determine the direction finding line corresponding to each direction finding station, and use bi-station cross positioning to obtain the set of intersection points of the direction finding lines of all direction finding stations;

[0007] Step 2: Use the high-density point extraction algorithm to determine the high-density points in the intersection point set, and obtain the high-density point set;

[0008] Step 3: Based on the cutoff radius corresponding to all high-density points in the high-density point set, cluster the high-density points in the high-density point set into clusters to form a cluster set;

[0009] Step 4: Calculate the azimuth angle of the cluster center of each cluster in the cluster set relative to each direction finding station. Based on the calculation results, filter and merge the clusters in the cluster set to determine the final cluster set.

[0010] Step 5: Obtain the target's location result based on the average position of each cluster in the final cluster set.

[0011] In one embodiment of the present invention, step 2 includes:

[0012] Step 2.1: Initialize the parameters of the high-density point extraction algorithm. The parameters include the cutoff radius dc, the cutoff radius increment step, the upper threshold of the cutoff radius MaxDc, and the lower threshold of the local density MinPts. The initial value of the cutoff radius dc is zero.

[0013] Step 2.2: Update the cutoff radius dc according to dc = dc + step, and determine whether the current cutoff radius dc satisfies dc ≤ MaxDc. If it does, proceed to step 2.3; otherwise, end the high-density point extraction algorithm and proceed to step 3.

[0014] Step 2.3: Calculate the Euclidean distance between each intersection point in the current set of intersection points to obtain the two-dimensional matrix dis of the Euclidean distance;

[0015] Step 2.4: Calculate the local density of each intersection point based on the two-dimensional matrix dis and the current cutoff radius dc, and determine the maximum value of the local density of all intersection points MaxPts. Determine whether the maximum value of the local density MaxPts satisfies MaxPts≥MinPts. If it satisfies, proceed to step 2.5; otherwise, return to step 2.2.

[0016] Step 2.5: Calculate the relative distance of each intersection point based on the two-dimensional matrix dis. Calculate the Gini coefficient of each intersection point based on the relative distance of each intersection point. Add the intersection point with the largest Gini coefficient as a high-density point to the high-density point set. Record the cutoff radius dc corresponding to the high-density point. Then update the intersection point set and return to step 2.2.

[0017] In one embodiment of the present invention, in step 2.4, the local density of each intersection point is calculated according to the following formula:

[0018] ρ i =∑ j≠i X(dis ij -dc);

[0019]

[0020] In the formula, ρ i Dis represents the local density at the i-th intersection point. ij dc represents the Euclidean distance between the i-th and j-th intersection points in the set of intersection points, and dc represents the cutoff radius.

[0021] In one embodiment of the present invention, in step 2.5, the relative distance between each intersection point is calculated according to the following formula:

[0022]

[0023] In the formula, rd i I represents the relative distance between the i-th intersection points. i This represents the set of intersection points whose local density is greater than that of the i-th intersection point. This represents the empty set.

[0024] In one embodiment of the present invention, in step 2.5, the Gini coefficient of each intersection point is calculated according to the following formula:

[0025] P i =ρ i *rd i ;

[0026] In the formula, P i Let represent the Gini coefficient at the i-th intersection point.

[0027] In one embodiment of the present invention, step 3 includes:

[0028] Step 3.1: Record the initial state of all high-density points in the high-density point set as unclustered, and arrange all high-density points in descending order of cutoff radius;

[0029] Step 3.2: Traverse each high-density point in the sorted high-density point set in order. If the state of the high-density point is unclustered, change its state to clustered. Determine whether the Euclidean distance between the other unclustered high-density points in the sorted high-density point set and the currently traversed high-density point is within the cutoff radius of the currently traversed high-density point. If so, change the state of the unclustered high-density point to clustered and cluster it with the currently traversed high-density point to form a cluster. After traversing the high-density point set, the cluster set is obtained.

[0030] In one embodiment of the present invention, step 4 includes:

[0031] Step 4.1: Calculate the average coordinates of all points in each cluster of the cluster set to obtain the cluster center;

[0032] Step 4.2: Calculate the azimuth angle of the cluster center relative to each direction finding station for each cluster;

[0033] Step 4.3: Calculate the error between the azimuth angle of the cluster center relative to each direction finding station and the initial measurement value of each direction finding station for each target, and obtain the azimuth angle error;

[0034] Step 4.4: Based on the azimuth error, remove clusters that do not meet the 3σ test principle, and merge clusters belonging to the same set of measurement data to obtain the final cluster set.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0036] The passive multi-station multi-target cross-localization method for removing false points of the present invention, based on the characteristic that the density near real target points is significantly higher than that of false points, effectively reduces the computational complexity of subsequent clustering while maintaining accuracy and false point removal performance as much as possible. The method of the present invention is simple and effective, and can complete the target localization using only the angle information measured by multiple stations and its own position.

[0037] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described in detail below with reference to the accompanying drawings. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of a passive multi-station multi-target cross-location method for removing false points provided in an embodiment of the present invention. Detailed Implementation

[0039] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the following describes in detail a passive multi-station multi-target cross-location method for removing false points according to the present invention, in conjunction with the accompanying drawings and specific embodiments.

[0040] The foregoing and other technical contents, features, and effects of the present invention will be clearly presented in the following detailed description of specific embodiments in conjunction with the accompanying drawings. Through the description of the specific embodiments, a more in-depth and concrete understanding can be gained of the technical means and effects adopted by the present invention to achieve its intended purpose. However, the accompanying drawings are for reference and illustration only and are not intended to limit the technical solutions of the present invention.

[0041] Example 1

[0042] Please see Figure 1 , Figure 1 This is a schematic diagram of a passive multi-station multi-target cross-location method for removing false points provided in an embodiment of the present invention. As shown in the figure, the passive multi-station multi-target cross-location method for removing false points in this embodiment includes:

[0043] Step 1: Based on the angle of arrival information of each target obtained by each direction finding station, determine the direction finding line corresponding to each direction finding station, and use bi-station cross positioning to obtain the set of intersection points of the direction finding lines of all direction finding stations;

[0044] For example, suppose there are N targets and M direction finding stations. For each of the M direction finding stations, N direction finding lines can be obtained in each measurement. Every two direction finding stations can form a two-station system, which has a total of There are several combinations, and for each combination, N can be determined using bi-station cross-location. 2 If there are 10 intersection points, then the set of intersection points of the direction finding lines from all direction finding stations includes 100 intersection points. Intersection points.

[0045] Step 2: Use the high-density point extraction algorithm to determine the high-density points in the intersection point set, and obtain the high-density point set;

[0046] In an optional implementation, step 2 includes:

[0047] Step 2.1: Initialize the parameters of the high-density point extraction algorithm. The parameters include the cutoff radius dc, the cutoff radius increment step, the upper threshold of the cutoff radius MaxDc, and the lower threshold of the local density MinPts. The initial value of the cutoff radius dc is zero.

[0048] Step 2.2: Update the cutoff radius dc according to dc = dc + step, and determine whether the current cutoff radius dc satisfies dc ≤ MaxDc. If it does, proceed to step 2.3; otherwise, end the high-density point extraction algorithm and proceed to step 3.

[0049] Step 2.3: Calculate the Euclidean distance between each intersection point in the current set of intersection points to obtain the two-dimensional matrix dis of the Euclidean distance;

[0050] Step 2.4: Calculate the local density of each intersection point based on the two-dimensional matrix dis and the current cutoff radius dc, and determine the maximum value of the local density of all intersection points MaxPts. Determine whether the maximum value of the local density MaxPts satisfies MaxPts≥MinPts. If it satisfies, proceed to step 2.5; otherwise, return to step 2.2.

[0051] In this embodiment, the local density represents the number of other intersection points existing within the cutoff radius of the i intersection points. Specifically, the local density of each intersection point is calculated according to the following formula:

[0052] ρ i =∑ j≠i X(dis ij -dc) (1);

[0053]

[0054] In the formula, ρ i Dis represents the local density at the i-th intersection point. ijdc represents the Euclidean distance between the i-th and j-th intersection points in the set of intersection points, and dc represents the cutoff radius.

[0055] Step 2.5: Calculate the relative distance of each intersection point based on the two-dimensional matrix dis. Calculate the Gini coefficient of each intersection point based on the relative distance of each intersection point. Add the intersection point with the largest Gini coefficient as a high-density point to the high-density point set. Record the cutoff radius dc corresponding to the high-density point. Then update the intersection point set and return to step 2.2.

[0056] In this embodiment, the relative distance rd of each point is calculated based on the idea of ​​density peak clustering. i Specifically, the relative distance between each intersection point is calculated using the following formula:

[0057]

[0058] In the formula, rd i I represents the relative distance between the i-th intersection points. i This represents the set of intersection points whose local density is greater than that of the i-th intersection point. This represents the empty set.

[0059] For the i-th intersection point, the relative distance rd i This means that when the local density at the i-th intersection point is maximum, rd i Get dis ij The maximum value of rd is when there exists a local density high intersection point at the i-th intersection point. i Take the condition that j∈I i dis ij Minimum value.

[0060] In this embodiment, the Gini coefficient at each intersection point is calculated according to the following formula:

[0061] P i =ρ i *rd i (4);

[0062] In the formula, P i Let represent the Gini coefficient at the i-th intersection point.

[0063] In this embodiment, after adding the intersection with the largest Gini coefficient as a high-density point to the high-density point set, the intersection is removed from the intersection set to update the intersection set.

[0064] Step 3: Based on the cutoff radius corresponding to all high-density points in the high-density point set, cluster the high-density points in the high-density point set into clusters to form a cluster set;

[0065] In an optional implementation, step 3 includes:

[0066] Step 3.1: Denote the initial state of all high-density points in the high-density point set as unclustered, and arrange all high-density points in descending order of cutoff radius;

[0067] Step 3.2: Iterate through each high-density point in the sorted high-density point set in order. If the state of the high-density point is unclustered, change its state to clustered. Determine whether the Euclidean distance between the other unclustered high-density points in the sorted high-density point set and the currently traversed high-density point is within the cutoff radius of the currently traversed high-density point. If so, change the state of the unclustered high-density point to clustered and cluster it with the currently traversed high-density point to form a cluster. After traversing the high-density point set, the cluster set is obtained.

[0068] Step 4: Calculate the azimuth angle of the cluster center of each cluster relative to each direction finding station. Based on the calculation results, filter and merge the clusters in the cluster set to determine the final cluster set.

[0069] In an optional implementation, step 4 includes:

[0070] Step 4.1: Calculate the average coordinates of all points in each cluster in the cluster set to obtain the cluster center;

[0071] Step 4.2: Calculate the azimuth angle of the cluster center relative to each direction finding station for each cluster;

[0072] Step 4.3: Calculate the error between the azimuth of the cluster center relative to each direction finding station and the initial measurement value of each direction finding station for each target, and obtain the azimuth error;

[0073] Step 4.4: Based on the azimuth error, remove clusters that do not meet the 3σ test principle, and merge clusters belonging to the same set of measurement data to obtain the final cluster set.

[0074] For example, for the cluster center of each cluster in the cluster set, its azimuth angle α relative to the direction finding station can be calculated. m For each direction finding station, m∈1,2…M, the initial measurement value for N targets is α. mn Let m∈1,2…M, n∈1,2…N, and define the azimuth error of the cluster center and each direction finding station as min(α). m -α mn ).

[0075] If the cluster center satisfies the following condition, i.e., meets the 3σ principle, then the cluster is retained; otherwise, it is discarded.

[0076]

[0077] If we are looking for min(α)m -α mn When ), for each m, there can be an n that minimizes the error. For example, for two cluster centers p and k, let's set a variable sum, assuming it's... This indicates that the cluster center p is closest to the n1-th direction finding data from the m-th direction finding station. This indicates that cluster center k is closest to the n2-th direction finding data from the m-th direction finding station. If n1 and n2 are equal, let sum + 1, and compare for each direction finding station. Finally, if sum ≥ M - 1, determine that cluster centers p and k are target estimation points corresponding to the same set of measurement data, and merge the two clusters.

[0078] Step 5: Obtain the target's location result based on the average position of each cluster in the final cluster set.

[0079] Optionally, the average coordinates of all points in each cluster in the final cluster set are calculated as the target location result, that is, the cluster center of each cluster in the final cluster set is the estimated target location.

[0080] The passive multi-station multi-target cross-localization method for removing false points of the present invention, based on the characteristic that the density near real target points is significantly higher than that of false points, effectively reduces the computational complexity of subsequent clustering while maintaining accuracy and false point removal performance as much as possible. The method of the present invention is simple and effective, and can complete the target localization using only the angle information measured by multiple stations and its own position.

[0081] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations are intended to cover non-exclusive inclusion, such that an article or apparatus comprising a list of elements includes not only those elements but also other elements not expressly listed. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or apparatus that includes said element. Terms such as "connected" or "linked" are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0082] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A passive multi-station multi-target cross-location method for removing false points, characterized in that, include: Step 1: Based on the angle of arrival information of each target obtained by each direction finding station, determine the direction finding line corresponding to each direction finding station, and use bi-station cross positioning to obtain the set of intersection points of the direction finding lines of all direction finding stations; Step 2: Use the high-density point extraction algorithm to determine the high-density points in the intersection point set, and obtain the high-density point set; Step 3: Based on the cutoff radius corresponding to all high-density points in the high-density point set, cluster the high-density points in the high-density point set into clusters to form a cluster set; Step 3 includes: Step 3.1: Record the initial state of all high-density points in the high-density point set as unclustered, and arrange all high-density points in descending order of cutoff radius; Step 3.2: Traverse each high-density point in the sorted high-density point set in order. If the state of the high-density point is unclustered, change its state to clustered. Determine whether the Euclidean distance between the other unclustered high-density points in the sorted high-density point set and the currently traversed high-density point is within the cutoff radius of the currently traversed high-density point. If so, change the state of the unclustered high-density point to clustered and cluster it with the currently traversed high-density point to form a cluster. After traversing the high-density point set, the cluster set is obtained. Step 4: Calculate the azimuth angle of the cluster center of each cluster in the cluster set relative to each direction finding station. Based on the calculation results, filter and merge the clusters in the cluster set to determine the final cluster set. Step 5: Obtain the target's location result based on the average position of each cluster in the final cluster set.

2. The passive multi-station multi-target cross-location method for removing false points according to claim 1, characterized in that, Step 2 includes: Step 2.1: Initialize the parameters of the high-density point extraction algorithm, including the cutoff radius. Cutoff radius increment step Upper threshold of cutoff radius Lower threshold of local density Among them, the cutoff radius The initial value is zero; Step 2.2: According to For the cutoff radius Update and determine the current cutoff radius. Does it meet the requirements? If the conditions are met, proceed to step 2.3; otherwise, end the high-density point extraction algorithm and proceed to step 3. Step 2.3: Calculate the Euclidean distance between all intersection points in the current set of intersection points to obtain a two-dimensional matrix of Euclidean distances. ; Step 2.4: Based on the two-dimensional matrix and the current cutoff radius The local density at each intersection point is calculated, and the maximum local density at all intersection points is determined. Determine the maximum local density. Does it meet the requirements? If the condition is met, proceed to step 2.5; otherwise, return to step 2.

2. Step 2.5: Based on the two-dimensional matrix Calculate the relative distance of each intersection point, and based on the relative distance, calculate the Gini coefficient of each intersection point. Add the intersection point with the largest Gini coefficient to the high-density point set, and record the cutoff radius corresponding to that high-density point. After updating the set of intersection points, return to step 2.

2.

3. The passive multi-station multi-target cross-location method for removing false points according to claim 2, characterized in that, In step 2.4, the local density at each intersection point is calculated using the following formula: ； ； In the formula, Indicates the first i Local density at intersection points In the set of intersection points, the first... i The intersection point and the first j Euclidean distance between the intersection points Indicates the cutoff radius.

4. The passive multi-station multi-target cross-location method for removing false points according to claim 3, characterized in that, In step 2.5, the relative distance between each intersection point is calculated using the following formula: ； In the formula, Indicates the first i The relative distance between the intersection points Indicates the local density ratio of the first i A set of intersection points with a large number of intersection points This represents the empty set.

5. The passive multi-station multi-target cross-location method for removing false points according to claim 4, characterized in that, In step 2.5, the Gini coefficient at each intersection point is calculated using the following formula: ； In the formula, Indicates the first i The Gini coefficient at each intersection point.

6. The passive multi-station multi-target cross-location method for removing false points according to claim 1, characterized in that, Step 4 includes: Step 4.1: Calculate the average coordinates of all points in each cluster of the cluster set to obtain the cluster center; Step 4.2: Calculate the azimuth angle of the cluster center relative to each direction finding station for each cluster; Step 4.3: Calculate the error between the azimuth angle of the cluster center relative to each direction finding station and the initial measurement value of each direction finding station for each target, and obtain the azimuth angle error; Step 4.4: Based on the azimuth error, remove clusters that do not meet the 3σ test principle, and merge clusters belonging to the same set of measurement data to obtain the final cluster set.

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