摘要
分析了测量方差预先设定对强跟踪滤波算法的不利影响,提出了一种测量方差自学习修正的强跟踪滤波算法。该滤波算法能够充分利用传感器每次测量带来的新信息,同时,可以进一步优化测量方差,提高了对状态的估计精度,最后,通过仿真计算验证了该算法的有效性。
The influence of the variance of the measured error presupposed on the strong tracking filter is analysed,and a new modified algorithm is presented based on the self-learning and improvement of the variance of the measured error.This new algorithm can not only sufficiently utilize renewed information each time from sensor,but also optimize the variance of the measured error step by step.The accuracy of the state estimation is improved.Finally,the stimulation shows this algorithm can obviously improve the efficiency of maneuvering target tracking.
出处
《传感器技术》
CSCD
北大核心
2005年第6期65-68,共4页
Journal of Transducer Technology
基金
国家自然科学基金资助项目(60272027)
河南省高校杰出科研人才创新工程项目(2003KYCX003)
关键词
强跟踪滤波
测量方差
状态估计
STF(strong tracking filter)
variance of measured error
state estimation
