现代雷达2025，Vol.47Issue(2)：45-51,7.DOI:10.16592/j.cnki.1004-7859.20230514001
基于当前统计模型改进的机动目标自适应跟踪算法
Adaptive Tracking Algorithm of Maneuvering Target Based on Current Statistical Model
摘要
Abstract
Based on the analysis of the applicabilities and limitations of traditional current statistical models,an improved current statistical model adaptive filtering algorithm is proposed to address the problem of low tracking accuracy caused by the need to pre-set fixed values for acceleration limit and maneuvering frequency in the current statistical model algorithm,which cannot adaptively adjust algorithm parameters during maneuvering target tracking.During the maneuvering target tracking,the algorithm adaptively adjusts the variance of acceleration and maneuvering frequency through the relationship between the difference in position estimation and acceleration disturbance,as well as the mean of the current acceleration estimation value,and then adaptively adjusts the vari-ance of process noise.The results of Monte Carlo simulation experiments show that the improved algorithm can continuously and stably track targets,and has better performance than traditional algorithms in tracking weak and strong maneuvering targets,which improves the tracking accuracy.关键词
当前统计模型/自适应滤波/噪声方差/蒙特卡洛/机动目标跟踪Key words
current statistical model(CSM)/adaptive filtering/noise variance/Monte Carlo/maneuvering target tracking分类
电子信息工程引用本文复制引用
滕康,周勇..基于当前统计模型改进的机动目标自适应跟踪算法[J].现代雷达,2025,47(2):45-51,7.基金项目
国家自然科学基金资助项目(61601231) （61601231）
