非高斯噪声下的机动目标跟踪

本文讨论了被动传感器在随机干扰条件下进行机动目标跟踪的方法，其观测量包含非高斯噪声，也可能包含影响观测值的随机干扰。与基于Ｋａｌｍａｎ滤波的常见方法不同，采用动态规划算法进行多假设检验，从而估计目标的状态。仿真试验表明本文方法能有效地处理非高斯噪声情况下的目标跟踪问题，而基于Ｋａｌｍａｎ滤波的跟踪方法，比如ＥＫＦ则效果较差。

非高斯噪声下的机动目标跟踪

This paper considers the problem of tracking with multiple paseive sensors in the nonGaussian noise environrnent. The measurements possibly include random interferene and Glint noise. The methed of targt state estimation in this paper is a dynamic programming approach. Unlike the traditional method, we reduce the estimating problem to a multiple hypothesis-testing problem, and then use the dynamic programming algorithm to solve the problem of tracking with non-Gaussian noise.The simulation results show the superiority of new method.