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CN-121978673-A - Multi-radar collaborative optimization station arrangement method based on improved particle swarm optimization

CN121978673ACN 121978673 ACN121978673 ACN 121978673ACN-121978673-A

Abstract

The invention discloses a multi-radar collaborative optimization station arrangement method based on an improved particle swarm algorithm, and belongs to the technical field of radars. The method comprises the steps of constructing a passive time difference positioning model comprising an active radar station and a plurality of passive radar stations, wherein the active radar station is a radar station capable of emitting electromagnetic waves and is located at a coordinate origin, the passive radar station does not emit electromagnetic waves, the station arrangement position of the passive radar station is a position to be solved, the sum of positioning errors of a target to be tracked in a flight airspace of each passive radar station is minimum as a target, an objective function is constructed, and the station arrangement position of each passive radar station is solved based on the objective function and an improved particle swarm algorithm. The invention realizes high-efficiency and accurate target positioning and tracking.

Inventors

  • FU XINGYU
  • YANG ZUO
  • WANG JIABAO
  • LUO QIUYANG
  • HUANG CHENYU
  • HAN QINGSONG
  • SONG YUYANG
  • DENG CHUANG
  • YU HAI
  • Yao Haoxi
  • GAO YUYI
  • XIANG YU
  • LI SHUCHUN
  • WU HAN
  • YANG XINYI
  • LI XIANG
  • LIU ZHIJUN
  • LI YONGHU
  • JIANG XIAOQING
  • QIN GUOJIAN
  • LV HAO
  • LU DI

Assignees

  • 北京卫星导航中心

Dates

Publication Date
20260505
Application Date
20251230

Claims (8)

  1. 1. A multi-radar collaborative optimization station arrangement method based on an improved particle swarm algorithm is characterized by comprising the following steps: The method comprises the steps of S1, constructing a passive time difference positioning model comprising an active radar station and a plurality of passive radar stations, wherein the active radar station is a radar station capable of emitting electromagnetic waves and is positioned at a coordinate origin, the passive radar station does not emit the electromagnetic waves, and the station arrangement position of the passive radar station is a position to be solved; S2, constructing an objective function by taking the minimum sum of positioning errors of a flight airspace of a target to be tracked of each passive radar station as a target; And step S3, solving the station distribution position of each passive radar station based on the objective function and the improved particle swarm algorithm.
  2. 2. The method of claim 1, wherein the objective function is: Wherein, the As a function of the object to be processed, The function is the trace of the matrix of values, In order to design a matrix for the observation equation, Is transposed, is a kind of , , ) The position coordinates of the target are [ ] , , ) For the position coordinates of the 1 st passive radar station, 、 、 Position coordinates of the 2 nd passive radar station 、 、 ) For the position coordinates of the ith passive radar station, For the distance from the target to be tracked to the 1 st passive radar station, For the distance of the object to be tracked to the 2 nd passive radar station, For the distance of the object to be tracked to the ith passive radar station.
  3. 3. The method of claim 1, wherein the step S3 is to solve the station placement position of each passive radar station based on an objective function and an improved particle swarm algorithm, wherein: The improved particle swarm algorithm is based on the particle swarm algorithm, and the weight coefficient is modified into dynamic weight coefficient, and the self-learning coefficient in the particle swarm algorithm is used And social learning coefficient Modified to dynamic coefficients.
  4. 4. A method according to claim 3, characterized in that the weighting coefficients are modified to dynamic weighting coefficients, namely: Wherein w is a weight coefficient, Is a weight factor, N is the number of particles, For a locally optimal solution of the i1 st particle for the number of iterations k, Is a globally optimal solution.
  5. 5. The method of claim 4, wherein the self-learning coefficients in the particle swarm algorithm are calculated And social learning coefficient The modification to dynamic coefficients includes: Wherein, the The maximum iteration number; 、 the first maximum learning times and the second maximum learning times are respectively; 、 a first minimum learning factor and a second minimum learning factor, respectively; 、 the values of the self-learning coefficient and the social learning coefficient when the iteration number is k+1 are respectively.
  6. 6. A multi-radar collaborative optimization station arrangement device based on an improved particle swarm algorithm, which is characterized in that the device comprises: The initialization module is configured to construct a passive time difference positioning model comprising an active radar station and a plurality of passive radar stations, wherein the active radar station is a radar station capable of emitting electromagnetic waves and is positioned at a coordinate origin, the passive radar station does not emit electromagnetic waves, and the station arrangement position of the passive radar station is a position to be solved; The target function construction module is configured to construct a target function by taking the minimum sum of the positioning errors of the target flight airspace to be tracked of each passive radar station as a target; and the calculation module is configured to solve the station distribution position of each passive radar station based on the objective function and the improved particle swarm algorithm.
  7. 7. An electronic device, the device comprising: at least one processor, and A memory communicatively coupled to the at least one processor, wherein, The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
  8. 8. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-5.

Description

Multi-radar collaborative optimization station arrangement method based on improved particle swarm optimization Technical Field The invention belongs to the technical field of radars, and particularly relates to a multi-radar collaborative optimization station arrangement method based on an improved particle swarm algorithm. Background The multi-radar station arrangement planning is always an important problem of radar networking system research, and the excellent radar array station arrangement method can effectively improve the cooperative efficiency of multiple radars, complete the effective coverage of airspace and the stable tracking of targets, and has an important role in improving the task success rate. The multi-radar station arrangement planning has an important effect on finishing the effective coverage of an airspace and the stable tracking of targets, most of technical researches on the multi-radar station arrangement planning are more ideal at present, the whole tracking and positioning errors of the multi-radar station arrangement to the target flight airspace are not considered, the traditional algorithm is easy to fall into a locally optimal state in operation, and the situation of slow convergence is easy to occur in the later period of the searching process. At present, most technical researches aiming at multi-radar station arrangement planning are more ideal, the overall tracking and positioning errors of the multi-radar station arrangement to a target flight airspace are not considered, the traditional algorithm is easy to fall into a locally optimal state in operation, and the situation of slow convergence is easy to occur in the later period of the searching process. Therefore, the method for collaborative optimization of station arrangement for multiple radars still has some problems to be solved: (1) How to realize the minimum overall tracking and positioning error of the multi-radar station distribution to the target flight airspace; (2) How to realize the multi-radar collaborative optimization station arrangement with global property and high convergence speed. Disclosure of Invention The invention provides a multi-radar collaborative optimization station arrangement method based on an improved particle swarm algorithm, which solves the technical problems. The first aspect of the invention provides a multi-radar collaborative optimization station arrangement method based on an improved particle swarm algorithm, which comprises the following steps: The method comprises the steps of S1, constructing a passive time difference positioning model comprising an active radar station and a plurality of passive radar stations, wherein the active radar station is a radar station capable of emitting electromagnetic waves and is positioned at a coordinate origin, the passive radar station does not emit the electromagnetic waves, and the station arrangement position of the passive radar station is a position to be solved; S2, constructing an objective function by taking the minimum sum of positioning errors of a flight airspace of a target to be tracked of each passive radar station as a target; And step S3, solving the station distribution position of each passive radar station based on the objective function and the improved particle swarm algorithm. Preferably, the objective function is: Wherein, the As a function of the object to be processed,The function is the trace of the matrix of values,In order to design a matrix for the observation equation,Is transposed, is a kind of,, ) The position coordinates of the target are [ ],,) For the position coordinates of the 1 st passive radar station,、、Position coordinates of the 2 nd passive radar station、、) For the position coordinates of the ith passive radar station,For the distance from the target to be tracked to the 1 st passive radar station,For the distance of the object to be tracked to the 2 nd passive radar station,For the distance of the object to be tracked to the ith passive radar station. Preferably, the step S3 is to solve the station distribution position of each passive radar station based on an objective function and an improved particle swarm algorithm, wherein: The improved particle swarm algorithm is based on the particle swarm algorithm, and the weight coefficient is modified into dynamic weight coefficient, and the self-learning coefficient in the particle swarm algorithm is used And social learning coefficientModified to dynamic coefficients. Preferably, the weighting coefficients are modified to dynamic weighting coefficients, namely: Wherein w is a weight coefficient, Is a weight factor, N is the number of particles,For a locally optimal solution of the i1 st particle for the number of iterations k,Is a globally optimal solution. Preferably, the self-learning coefficient in the particle swarm algorithm is usedAnd social learning coefficientThe modification to dynamic coefficients includes: Wherein, the The maximum iterat