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Abstract: Noise statistics are essential for estimation performance. In practical situations, however, a priori information of noise statistics is often imperfect. Previous work on noise statistics identification in linear systems still requires initial prior knowledge of the noise. A novel approach is presented in this paper to solve this paradox. First, we apply the H_∞; filter to obtain the system state estimates without the common assumptions about the noise in conventional adaptive filters. Then by applying state estimates obtained from the H_∞; filter, better estimates of the noise mean and covariance can be achieved, which can improve the performance of estimation. The proposed approach makes the best use of the system knowledge without a priori information with modest computation cost, which makes it possible to be applied online. Finally, numerical examples are presented to show the efficiency of this approach.

This paper deals with the noise statistics estimation problem in target tracking. By introducing the H_∞ filter instead of other conventional filters, more accurate noise samples could be obtained, which would lead to more exact estimates of noise mean and covariance. Overall, this paper is interesting and of some significance.

目标跟踪中一种新的基于H_∞滤波器的噪声统计特征估计方法

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