Suboptimal Adaptive Filter for Discrete-Time Linear Stochastic Systems

Daebum CHOI  Vladimir SHIN  Jun IL AHN  Byung-Ha AHN  

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)
mean-square estimation,  Kalman filtering,  adaptive filtering,  Lainiotis' partition theorem,  multisensor,  

Full Text: PDF(748.4KB)>>
Buy this Article

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.