Adaptive suboptimal filtering of bilinear systems
作者:
XUESHAN YANG,
R. R- MOHLER,
R. M. BURTON,
期刊:
International Journal of Control
(Taylor Available online 1990)
卷期:
Volume 52,
issue 1
页码: 135-158
ISSN:0020-7179
年代: 1990
DOI:10.1080/00207179008953528
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
The optimal filter (minimum mean square error) of discrete bilinear stochastic systems with output feedback is studied here. The sequential filter of bilinear systems is derived for suboptimal adaptive estimation of the unknown aprioristate and observation-noise statistics simultaneously with the bilinear system state. The unbiased estimations of state-noise varianceQand observation-noise variance R are obtained under some usual conditions. For on-line operation, this paper gives the recursive form of this adaptive suboptimal filter (ASF). Computer simulations show that ASF approximates the minimum mean square error (MSE) filter very well, and ASF provides improved state estimates at little computing expense
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