航空学报 > 2023, Vol. 44 Issue (2): 326502-326502   doi: 10.7527/S1000-6893.2022.26502

基于改进粒子群的时差测向最优阵列布局

蒋平1,2,3, 屈秉男1,3, 丁华泽1, 马润泽1, 何为1()   

  1. 1.中国科学院 上海微系统与信息技术研究所 中国科学院无线传感网与通信重点实验室,上海  201800
    2.上海科技大学 信息科学与技术学院,上海  201210
    3.中国科学院大学,北京  100049
  • 收稿日期:2021-10-11 修回日期:2021-11-18 接受日期:2022-04-11 出版日期:2023-01-25 发布日期:2022-04-24
  • 通讯作者: 何为 E-mail:wei.he@mail.sim.ac.cn
  • 基金资助:
    中国科学院重点部署项目(KFZD-SW-437)

Optimal array structure for time difference direction finding based on improved particle swarm optimization

Ping JIANG1,2,3, Bingnan QU1,3, Huaze DING1, Runze MA1, Wei HE1()   

  1. 1.Key Laboratory of Wireless Sensor Network and Communication of Chinese Academy of Sciences,Shanghai Institute of Microsystem and Information Technology,Chinese Academy of Sciences,Shanghai  201800,China
    2.School of Information Science and Technology,Shanghai Tech University,Shanghai  201210,China
    3.University of Chinese Academy of Sciences,Beijing  100049,China
  • Received:2021-10-11 Revised:2021-11-18 Accepted:2022-04-11 Online:2023-01-25 Published:2022-04-24
  • Contact: Wei HE E-mail:wei.he@mail.sim.ac.cn
  • Supported by:
    Key Research Program of the Chinese Academy Science(KFZD-SW-437)

摘要:

为了减小阵列布局对测向算法精度的影响,提升在特定场景下目标的测向精度,提出基于竞争策略和差分进化策略的粒子群优化(PSO-CDE)算法,并基于PSO-CDE实现时差测向阵列优化。首先,基于时差测向的原理,以位置约束和基线约束设计传感器阵列,以均方误差构建适应度评价函数;其次,提出PSO-CDE算法来提高粒子群性能和鲁棒性,并基于PSO-CDE算法对阵列布局进行策略优化;最后,通过仿真靶场环境,得到不同条件下的优化阵列布局。仿真结果表明:优化后的阵列较规则阵列具有更高的目标测向精度。同时,对比分析最优阵列中阵列基线、阵元数量和时延误差对测向精度的影响,为实际场景中阵列布局优化策略的选择提供相应的参考依据。

关键词: 时差测向, 阵列优化, 粒子群, 差分进化, 竞争策略

Abstract:

To reduce the impact of the array structure on the accuracy of the direction finding algorithm and improve the accuracy of target direction finding in specific scenarios, a Particle Swarm Optimization algorithm with Competitive and Differential Evolution (PSO-CDE) is proposed to optimize the time difference direction finding array. First, according to the principle of time difference direction finding, the sensor array is designed with position and baseline constraints. The fitness evaluation function is constructed with the mean square error. Second, the PSO-CDE algorithm is proposed to improve particle swarm optimization performance and robustness and optimize the array structure. Finally, the optimized array structure under different conditions is obtained by simulating the target range environment. The results show that the optimized array has higher accuracy in target direction finding than the regular array. The influence of the array element baseline, the number of the elements and the time delay error in the optimal array on the accuracy of direction finding is also analyzed, which provides a reference for the selection of array structure optimization strategy in the actual scene.

Key words: time difference direction finding, array optimization, particle swarm, differential evolution, competitive strategy

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