標題: Recursive filtering with non-Gaussian noises
作者: Wu, WR
Kundu, A
電信工程研究所
Institute of Communications Engineering
公開日期: 1-六月-1996
摘要: The Kalman filter is the optimal recursive filter, although its optimality can only be claimed under the Gaussian noise environment, In this paper, we consider the problem of recursive filtering with non-Gaussian noises, One of the most promising schemes, which was proposed by Masreliez, uses the nonlinear score function as the correction term in the state estimate, Unfortunately, the score function cannot be easily implemented except for simple cases, In this paper, a new method for efficient evaluation of the score function is developed, The method employs an adaptive normal expansion to expand the score function followed by truncation of the higher order terms, Consequently, the score function can be approximated by a few central moments, The normal expansion is made adaptive by using the concept of conjugate recentering and the saddle point method, It is shown that the approximation is satisfactory, and the method is simple and practically feasible, Experimental results are reported to demonstrate the effectiveness of the new algorithm.
URI: http://dx.doi.org/10.1109/78.506611
http://hdl.handle.net/11536/1243
ISSN: 1053-587X
DOI: 10.1109/78.506611
期刊: IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume: 44
Issue: 6
起始頁: 1454
結束頁: 1468
顯示於類別:期刊論文


文件中的檔案:

  1. A1996UU13400012.pdf