基于粒子群算法的多目标跟踪优化传感器控制策略

兰州理工大学学报 ›› 2025, Vol. 51 ›› Issue (2): 88-93.

• 自动化技术与计算机技术 • 上一篇下一篇

基于粒子群算法的多目标跟踪优化传感器控制策略

陈辉^*1, 魏凤旗¹, 赵永红², 彭天曙³

1.兰州理工大学电气工程与信息工程学院, 甘肃兰州 730050;
2.甘肃长风电子科技有限责任公司, 甘肃兰州 730070;
3.甘肃省计算中心, 甘肃兰州 730000

收稿日期:2022-07-28 出版日期:2025-04-28 发布日期:2025-04-29
通讯作者: 陈辉(1978-),男,山西闻喜人,博士,教授,博导.Email:huich78@hotmail.com
基金资助:
国家自然科学基金(62163023,61873116,62366031,62363023),甘肃省基础研究创新群体(25JRRA058),中央引导地方科技发展资金项目(25ZYJA040),2024年度甘肃省重点人才项目(2024RCXM86),2023年度甘肃省军民融合发展专项资金

Multi-target tracking optimization sensor control strategy based on particle swarm optimization algorithm

CHEN Hui¹, WEI Feng-qi¹, ZHAO Yong-hong², PENG Tian-shu³

1. College of Electrical and Information Engineering,Lanzhou University of Technology, Lanzhou 730050, China;
2. Gansu Province Changfeng Electronic Technology Co. LTD., Lanzhou 730070, China;
3. Gansu Provincial Computing Center, Lanzhou 730000, China

Received:2022-07-28 Online:2025-04-28 Published:2025-04-29

摘要/Abstract

摘要： 针对多目标跟踪优化问题,提出一种基于粒子群算法的传感器控制策略.首先由泊松多伯努利混合(PMBM)滤波器的预测过程得到多目标预测状态,然后以此为先验信息通过粒子群算法以最大限度地接近各目标为准则求解传感器最优观测位置,并由传感器捕捉优质量测信息,最后由PMBM滤波器的更新过程得到优化多目标后验状态.仿真实验对比了多目标跟踪优化的效果,结果表明该传感器控制策略有更好的多目标跟踪精度.

关键词: 传感器控制, 粒子群算法, 多目标跟踪, 泊松多伯努利混合, 最优观测

Abstract: This paper presents a sensor control strategy based on particle swarm optimization for multi-target tracking optimization. Among the multi-target tracking methods, the Poisson multi Bernoulli mixture (PMBM) filter is widely used for its effective representation of undetected (existing but not detected) target information and more efficient recursive structure. First, the multi-target prediction state is obtained through the prediction process of the PMBM filter. Then, taking this as a priori information, the particle swarm optimization algorithm is employed to solve the optimal observation position of the sensor based on the criterion of maximizing the proximity to each target. The sensor then captures the optimal quality measurement information. Finally, the optimized multi-objective posterior state is obtained by the update process of the PMBM filter. Simulation experiments were conducted to compare the effectiveness of multi-target tracking optimization, and the results show that the proposed sensor control strategy has better multi-target tracking accuracy.

Key words: sensor control, PSO, multi-target tracking, PMBM, optimal observation

中图分类号:

TP274

陈辉, 魏凤旗, 赵永红, 彭天曙. 基于粒子群算法的多目标跟踪优化传感器控制策略[J]. 兰州理工大学学报, 2025, 51(2): 88-93.

CHEN Hui, WEI Feng-qi, ZHAO Yong-hong, PENG Tian-shu. Multi-target tracking optimization sensor control strategy based on particle swarm optimization algorithm[J]. Journal of Lanzhou University of Technology, 2025, 51(2): 88-93.

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基于粒子群算法的多目标跟踪优化传感器控制策略

Multi-target tracking optimization sensor control strategy based on particle swarm optimization algorithm

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