[关键词]
[摘要]
针对不敏卡尔曼滤波器在递推过程中的数值不稳定性以及系统发生突变时跟踪效果不佳的问题,提出一种新的机动目标自适应跟踪算法——基于强跟踪的平方根不敏卡尔曼滤波器(STF-SRUKF)。该算法一方面基于平方根滤波的思想,在递推过程中采用协方差矩阵的平方根代替协方差矩阵本身,以保证数值计算的稳定性;另一方面,基于强跟踪滤波的思想,在递推过程中引入时变渐消因子,实时调节增益矩阵,以增强目标运动发生突变时的跟踪能力。仿真结果表明,STF-SRUKF算法对于突发机动的目标运动模型具有良好的跟踪效果,而且具有较好的稳定性。
[Key word]
[Abstract]
To solve the numerical instability in the recursive process of unscented Kalman filter (UKF), as well as the unsatisfactory performance in case of abrupt changes, a new adaptive target tracking method, called square root unscented Kalman filter based on strong tracking (STF-SRUKF), is presented. On the one hand, inspired by the idea of square-root filter, the square root of the covariance matrix is substituted for the covariance matrix itself in the recursive process, to guarantee numerical stability. On the other hand, based on the idea of strong tracking filter, a time-varied fading factor is introduced into the recursive process, which is helpful to adjust the gain matrix timely, and thus enabling STF-SRUKF more power to deal with sudden changes. Experimental results demonstrate that STF-SRUKF performs well and steadily, especially when target motion changes suddenly.
[中图分类号]
[基金项目]
国家自然科学基金重点项目;中央高校基本科研业务费专项资金