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CN-116466293-B - Passive multi-station multi-target cross positioning method for removing pseudo points

CN116466293BCN 116466293 BCN116466293 BCN 116466293BCN-116466293-B

Abstract

The invention relates to a passive multi-station multi-target cross positioning method for removing pseudo points, which comprises the steps of determining a direction finding line corresponding to each direction finding station according to arrival angle information of each target obtained by each direction finding station, obtaining an intersection point set of the direction finding lines of all the direction finding stations by utilizing double-station cross positioning, determining high-density points in the intersection point set by utilizing a high-density point extraction algorithm to obtain a high-density point set, clustering the high-density points in the high-density point set into clusters according to the truncated radius corresponding to all the high-density points in the high-density point set to form a cluster set, calculating the azimuth angle of the cluster center of each cluster of the obtained cluster set relative to each direction finding station, screening and merging the clusters in the cluster set according to a calculation result, and obtaining the positioning result of the target according to the average position of each cluster in the final cluster set. The invention is simple and effective, and can complete the positioning of the target only through the angle information and the self position of the multi-station measurement.

Inventors

  • ZHU LINA
  • HOU XUAN
  • WEI YAQI

Assignees

  • 西安电子科技大学

Dates

Publication Date
20260505
Application Date
20230314

Claims (6)

  1. 1. A passive multi-station multi-target cross positioning method for removing pseudo points is characterized by comprising the following steps: step 1, determining a direction-finding line corresponding to each direction-finding station according to arrival angle information of each target acquired by each direction-finding station, and obtaining intersection point sets of the direction-finding lines of all the direction-finding stations by using double-station cross positioning; step 2, determining high-density points in the intersection point set by using a high-density point extraction algorithm to obtain a high-density point set; Step 3, clustering the high-density points in the high-density point set into clusters according to the truncated radiuses corresponding to all the high-density points in the high-density point set to form a cluster set, wherein the step 3 comprises the following steps: Step 3.1, marking the initial states of all high-density points in the high-density point set as non-clustered, and arranging all the high-density points in descending order according to the cutting radius from large to small; Step 3.2, sequentially traversing each high-density point in the ordered high-density point set, if the state of the high-density point is not clustered, changing the state of the high-density point into clustered, judging whether Euclidean distances between other non-clustered high-density points in the ordered high-density point set and the currently traversed high-density point are within the range of the cut-off radius of the currently traversed high-density point, if so, changing the state of the non-clustered high-density point into clustered, clustering the non-clustered high-density point with the currently traversed high-density point to form a cluster, and completing the traversing of the high-density point set to obtain a cluster set; Step 4, calculating azimuth angles of the cluster centers of the clusters relative to the direction-finding stations, screening and combining the clusters in the cluster set according to calculation results, and determining a final cluster set; And 5, acquiring a positioning result of the target according to the average position of each cluster in the final cluster set.
  2. 2. The passive multi-station multi-target cross-positioning method for removing pseudo points according to claim 1, wherein the step 2 comprises: step 2.1 initializing parameters of a high density point extraction algorithm, said parameters comprising a truncation radius Cut-off radius self-increasing step length Upper threshold value of cutoff radius Lower threshold of local density Wherein the radius of truncation Is zero; Step 2.2 according to For the cut-off radius Updating and judging the current cut-off radius Whether or not to meet If yes, executing the step 2.3, otherwise, ending the high-density point extraction algorithm, and executing the step 3; step 2.3, calculating Euclidean distance between each intersection point in the current intersection point set to obtain a two-dimensional matrix of Euclidean distance ; Step 2.4. According to the two-dimensional matrix And the current cutoff radius Calculating to obtain local density of each intersection point, and determining maximum value of local densities of all intersection points Judging the maximum value of local density Whether or not to meet If yes, executing the step 2.5, otherwise returning to the step 2.2; Step 2.5. According to the two-dimensional matrix Calculating the relative distance of each intersection point, calculating the coefficient of the foundation of each intersection point according to the relative distance of each intersection point, adding the intersection point with the maximum coefficient of the foundation as a high-density point into a high-density point set, and simultaneously recording the corresponding truncated radius of the high-density point And then, updating the intersection point set and returning to the step 2.2.
  3. 3. The passive multi-station multi-target cross-positioning method for removing false points according to claim 2, wherein in said step 2.4, the local density of each intersection point is calculated according to the following formula: ; ; In the formula, Representing the local density of the i-th intersection point, Representing the euclidean distance between the i-th intersection and the j-th intersection in the set of intersections, Representing the radius of the truncation.
  4. 4. A passive multi-station multi-target cross-positioning method for removing false points according to claim 3, wherein in said step 2.5, the relative distance of each intersection point is calculated according to the following formula: ; In the formula, Representing the relative distance of the i-th intersection point, Represents a set of intersections with a local density greater than the i-th intersection, Representing an empty set.
  5. 5. The passive multi-station multi-target cross positioning method for removing false points according to claim 4, wherein in said step 2.5, the coefficient of the kunit of each cross point is calculated according to the following formula: ; In the formula, The coefficient of kunith representing the i-th intersection.
  6. 6. The passive multi-station multi-target cross-positioning method for removing pseudo points according to claim 1, wherein the step 4 comprises: calculating the average value of the coordinates of all points of each cluster in the cluster set to obtain the cluster center of the cluster; step 4.2, calculating to obtain the azimuth angle of the cluster center of each cluster relative to each direction-finding station; Step 4.3, carrying out error calculation on the azimuth angle of the cluster center relative to each direction-finding station and the initial measurement value of each direction-finding station relative to each target to obtain an azimuth angle error; And 4.4, eliminating clusters which do not accord with the 3 sigma inspection principle according to the azimuth angle error, and merging clusters belonging to the same group of measurement data to obtain a final cluster set.

Description

Passive multi-station multi-target cross positioning method for removing pseudo points Technical Field The invention belongs to the technical field of electronic countermeasure, and particularly relates to a passive multi-station multi-target cross positioning method for removing pseudo points. Background The role and play of electronic countermeasure in modern warfare is continuously enhanced, and the positioning of the radiation source is an important subject of electronic countermeasure. Although the active positioning technology has the advantages of all weather and high precision, the active positioning technology is also easily detected by enemies, so that the active positioning technology is attacked by soft kills of electronic interference of the enemies or hard kills of weapons such as anti-radiation missiles, and the active positioning technology has great influence on positioning precision and even threatens the safety of a positioning system. The passive positioning is a passive detection scout positioning, which does not emit signals and only receives signals from external radiation, so that the passive detection scout positioning has extremely strong concealment. The method can obtain relevant parameters for calculating the target position information from the received signals, and further realize target positioning. In passive positioning, direction-finding cross positioning is widely applied due to simple equipment, reliable azimuth information, long detection distance and high system sensitivity. However, in a multi-target positioning scenario, multiple sensors can generate a large number of false points when using multi-target azimuth measurements for cross positioning. Disclosure of Invention In order to solve the problems in the prior art, the invention provides a passive multi-station multi-target cross positioning method for removing pseudo points. The technical problems to be solved by the invention are realized by the following technical scheme: The invention provides a passive multi-station multi-target cross positioning method for removing pseudo points, which comprises the following steps: step 1, determining a direction-finding line corresponding to each direction-finding station according to arrival angle information of each target acquired by each direction-finding station, and obtaining intersection point sets of the direction-finding lines of all the direction-finding stations by using double-station cross positioning; step 2, determining high-density points in the intersection point set by using a high-density point extraction algorithm to obtain a high-density point set; Step 3, clustering the high-density points in the high-density point set into clusters according to the corresponding truncated radiuses of all the high-density points in the high-density point set to form a cluster set; Step 4, calculating azimuth angles of the cluster centers of the clusters relative to the direction-finding stations, screening and combining the clusters in the cluster set according to calculation results, and determining a final cluster set; And 5, acquiring a positioning result of the target according to the average position of each cluster in the final cluster set. In one embodiment of the present invention, the step 2 includes: Initializing parameters of a high-density point extraction algorithm, wherein the parameters comprise a truncation radius dc, a truncation radius self-increasing step length step, an upper threshold MaxDc of the truncation radius and a lower threshold MinPts of local density, and the initial value of the truncation radius dc is zero; Step 2.2, updating the cut-off radius dc according to dc=dc+step, judging whether the current cut-off radius dc is less than or equal to MaxDc, if so, executing step 2.3, otherwise, ending the high-density point extraction algorithm, and executing step 3; Step 2.3, calculating Euclidean distance between each intersection point in the current intersection point set to obtain a two-dimensional matrix dis of Euclidean distance; Step 2.4, calculating to obtain the local density of each intersection point according to the two-dimensional matrix dis and the current cut-off radius dc, determining the maximum value MaxPts of the local densities of all intersection points, judging whether the maximum value MaxPts of the local densities meets MaxPts and is more than or equal to MinPts, if so, executing step 2.5, otherwise, returning to step 2.2; And 2.5, calculating the relative distance of each intersection point according to the two-dimensional matrix dis, calculating the coefficient of the foundation of each intersection point according to the relative distance of each intersection point, adding the intersection point with the maximum coefficient of the foundation as a high-density point into a high-density point set, recording the truncation radius dc corresponding to the high-density point, updating the intersection point set, and retur