一种带未知时变系统噪声水平的目标跟踪滤波器

在机动目标跟踪中，为了保证Kalman滤波器的数值稳定性和最优性，未知的时变系统噪声水平需要在线估计，但已有方法主要针对平稳或统计特性缓变的噪声过程。在Sage-Husa系统噪声水平自适应估计算法的基础上，通过引入基于新息的滤波器发散检测判据和利用强跟踪滤波器的思想，提出了一种系统噪声水平估计值的时变调节因子阵来抑制因系统噪声水平突变而引起的滤波器可能出现的发散问题。Monte-Carlo仿真结果表明，该算法不仅数值稳定性好，同时目标的跟踪精度也得到明显改善。

A Target Tracking Filter with Unknown Time-varying System Noise Variance

In maneuvering target tracking, to ensure the numerical stability and optimality of Kalman filter, the unknown time-varying system noise variance needs to be estimated adaptively, but the existing methods are mostly limited to systems with stationary or slowly-varying noises. Based on the Sage-Husa adaptive estimator of system noise variance, by introducing the innovation-based detection criterion for filter divergence and using the idea of strong tracking filter, a time-varying scaling factor matrix multiplied by the system noise variance estimate is proposed in this paper, which can restrain the likely divergence of the tracking filter due to the sudden jumps of the system noise variance. Monte-Carlo simulation results show that the new proposed algorithm not only has better numerical stability, but also can improve the tracking precision greatly.