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Suboptimal Adaptive Filter for Discrete-Time Linear Stochastic Systems Daebum CHOI Vladimir SHIN Jun IL AHN Byung-Ha AHN Publication IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences Vol.E88-A No.3 pp.620-625 Publication Date: 2005/03/01 Online ISSN: DOI: 10.1093/ietfec/e88-a.3.620 Print ISSN: 0916-8508 Type of Manuscript: Special Section PAPER (Special Section on Adaptive Signal Processing and Its Applications) Category: Keyword: mean-square estimation, Kalman filtering, adaptive filtering, Lainiotis' partition theorem, multisensor, Full Text: PDF(748.4KB)>>
Summary: This paper considers the problem of recursive filtering for linear discrete-time systems with uncertain observation. A new approximate adaptive filter with a parallel structure is herein proposed. It is based on the optimal mean square combination of arbitrary number of correlated estimates which is also derived. The equation for error covariance characterizing the mean-square accuracy of the new filter is derived. In consequence of parallel structure of the filtering equations the parallel computers can be used for their design. It is shown that this filter is very effective for multisensor systems containing different types of sensors. A practical implementation issue to consider this filter is also addressed. Example demonstrates the accuracy and efficiency of the proposed filter. | ||||||||||
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