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

作　　者：滕康 周勇[1] TENG Kang;ZHOU Yong(School of Electronics and Information Engineering,Nanjing University of Information Science and Technology,Nanjing Jiangsu 210044,China)

机构地区：[1]南京信息工程大学电子与信息工程学院,江苏南京210044

出　　处：《现代雷达》2025年第2期45-51,共7页Modern Radar

基　　金：国家自然科学基金资助项目(61601231)。

摘　　要：针对当前统计模型算法对加速度极限值和机动频率需要预先设置成固定值,在机动目标跟踪过程中无法自适应调整算法参数带来的跟踪精度不高的问题,文中在分析传统当前统计模型适用范围与局限性的基础上,提出了一种改进的当前统计模型自适应滤波算法来实现对传统当前统计模型的改进。算法通过位置估计量差值与加速度扰动量的关系以及当前时刻加速度估计值的均值,在机动目标跟踪过程中对加速度方差和机动频率进行自适应调整,进而自适应调整过程噪声方差。蒙特卡洛模拟仿真实验的结果表明:改进后的算法能持续稳定地跟踪目标,并且对弱机动目标、强机动目标的跟踪较传统算法均具有更好的效果,提高了跟踪精度。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 variance 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 

分 类 号：TN953[电子电信—信号与信息处理]