基于自适应平滑KF-PDA算法的舰船单目标跟踪
Single Target Tracking of Ships Based on Adaptive Smoothing KF-PDA Algorithm
任明亮 1贾志强 2盛庆红 2孙珠磊2
作者信息
- 1. 中国科学技术馆,北京 100012;南京航空航天大学航天学院,南京 211106
- 2. 南京航空航天大学航天学院,南京 211106
- 折叠
摘要
针对概率数据互联(Probability data association,PDA)算法在杂波环境下计算复杂度高的问题,设计了一种基于PDA算法的数据关联方法,当波门内量测点数量大于阈值时,采用PDA算法更新目标状态;当波门内量测点数量小于等于阈值时,采用最近邻思想筛选目标量测点,接着利用卡尔曼滤波(Kalman filter,KF)算法实现杂波环境下的快速滤波更新.在此基础上,通过自适应区间平滑方法,动态修正平滑区间,实现整体状态估计的反向平滑,从而提升算法的精度.不同杂波环境下的实验结果表明,本文方法相较于PDA算法与KF-PDA算法,在保证跟踪效率的同时,有效提升了系统状态的估计精度,验证了该方法的鲁棒性和有效性.
Abstract
In view of high computational complexity of the probability data association(PDA)algorithm in cluttered environments,a data association method based on the PDA algorithm is designed.When the number of measurement points in the wavegate exceeds a certain threshold,the PDA algorithm is employed to update the target state.When the number of measurement points falls below or equals the threshold,a nearest-neighbor approach is used to filter the target measurement points.Subsequently,the Kalman filter(KF)algorithm is utilized to achieve fast filtering updates in cluttered environments.Additionally,the paper proposes an adaptive interval smoothing method that dynamically corrects the smoothing interval to achieve reverse smoothing of the overall state estimation.This approach aims to improve the algorithm's accuracy.Experimental results of various clutter environments demonstrate that the proposed method effectively enhances the estimation accuracy of the system state while ensuring tracking efficiency.Moreover,the results validate the robustness and effectiveness of the method compared to the PDA algorithm and the KF-PDA algorithm.
关键词
自适应平滑区间/卡尔曼滤波算法/概率数据互联算法/状态估计Key words
adaptive smoothing interval/Kalman filtering algorithm/probability data association(PDA)algorithm/state estimation引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据