摘要：

针对闪烁噪声下存在未知机动的空间目标跟踪问题，将自适应鲁棒滤波技术嵌入到无迹卡尔曼滤波，设计自适应鲁棒无迹卡尔曼滤波(ARUKF)，再利用ARUKF产生粒子滤波的重要性密度函数，从而得到一种自适应鲁棒无迹粒子滤波(ARUPF)算法。将ARUPF与瞬态跟踪模型相结合，对空间机动目标进行自主跟踪。实验结果表明，该算法在跟踪精度和鲁棒性方面优于传统的跟踪算法。

关键词: 机动目标跟踪, 自适应鲁棒滤波, 无迹卡尔曼滤波, 粒子滤波, 闪烁噪声, 瞬态模型

Abstract:

For solving the problem of tracking space target that has unknown maneuver under glint noise, an Adaptive Robust Unscented Particle Filtering(ARUPF) algorithm is proposed in this paper. Adaptive Robust Unscented Kalman Filtering(ARUKF) algorithm is designed by embedding adaptive robust filtering technique into Unscented Kalman filtering(UKF). ARUPF is developed by using ARUKF to generate the importance density function in Particle Filtering(PF). Combined with transient tracking model, ARUPF is applied for space maneuvering target autonomous tracking. Experimental result shows that the proposed algorithm improves the tracking accuracy and robustness, contrasting to the existing filtering algorithms.

Key words: maneuvering target tracking, adaptive robust filtering, Unscented Kalman Filtering(UKF), Particle Filtering(PF), glint noise, transient model

中图分类号: 

• TP312