基于粒子群优化算法的目标运动参数估计

官善政; 陈韶华; 陈 川

doi:10.11993/j.issn.2096-3920.2018.05.005

基于粒子群优化算法的目标运动参数估计

doi: 10.11993/j.issn.2096-3920.2018.05.005

中国船舶重工集团公司第710研究所, 湖北宜昌, 443003

基金项目: 国家重点研发计划(2016YFC1400200)

详细信息

作者简介:
官善政(1993-), 男, 硕士, 研究方向为水下探测与控制技术.

中图分类号: TJ630.34; TN911.7; TB566
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出版历程
- 刊出日期: 2018-10-31

Target Movement Parameter Estimation Based on Particle Swarm Optimization Algorithm

The 710 Research Institute, China Shipbuilding Industry Corporation, Yichang 443003, China

摘要

摘要: 粒子群优化算法具有易于实现、可并行计算、收敛速度快且全局收敛等优点, 文中结合水下目标被动跟踪定位系统对目标运动参数估计的实时性和精确性需求, 提出了一种利用目标方位信息和多普勒频移信息估计目标运动参数的方法。该方法通过测量目标的方位角变化和多普勒频移, 基于最小均方误差(MMSE)准则建立参数估计方程, 并依靠粒子群优化(PSO)算法确定一组可使均方误差函数最小的运动参数, 实现对目标实时位置、航速、正横距离的精确估计。仿真结果表明, 与扩展卡尔曼滤波(EKF)算法对比, 在相同参数估计精度条件下, 粒子群优化算法能更快收敛; 对于小正横、高航速目标, 该算法能够在目标过正横前准确给出目标正横通过距离的预报, 并在目标过正横后提供较高的跟踪精度。文中工作可为水下目标被动跟踪和运动参数精确估计提供参考。
- 水下目标 /
- 被动定位 /
- 参数估计 /
- 粒子群优化 /
- 扩展卡尔曼滤波
Abstract: Considering the demand for real-time property and accuracy of an underwater target passive tracking and location system, a target parameter estimation method is proposed by using the target’s azimuth and Doppler shift information. With the measured target’s azimuth variation and Doppler frequency shift, a parameter estimation equation is established based on the minimum mean square error(MMSE) criterion, and a set of motion parameters is determined by the particle swarm optimization(PSO) algorithm to minimize the mean square error function, thus the accurate estimations of target’s real-time position, velocity, and closest passing distance are achieved. Simulation results show that the PSO algorithm converges more rapidly with equivalent convergence precision compared with the extended Kalman filter algorithm; and for the close-distance and high-speed target, the PSO algorithm can provide accurate prediction of target’s closest passing distance and high tracking precision before and after it passes by, respectively. This research is expected to provide a reference for passive tracking of underwater target and accurate estimation of movement parameters.
- underwater target /
- passive location /
- parameter estimation /
- particle swarm optimization(PSO) /
- extended Kalman filter(EKF)

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参考文献(10)

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