Journal
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
Volume 45, Issue 7, Pages 1548-1562Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207721.2014.909093
Keywords
random parameter matrices; correlated noises; optimal least-squares estimation; innovation approach
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Funding
- Ministerio de Ciencia e Innovacion [FPU programme] [MTM2011-24718]
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This paper addresses the optimal least-squares linear estimation problem for a class of discrete-time stochastic systems with random parameter matrices and correlated additive noises. The system presents the following main features: (1) one-step correlated and cross-correlated random parameter matrices in the observation equation are assumed; (2) the process and measurement noises are one-step autocorrelated and two-step cross-correlated. Using an innovation approach and these correlation assumptions, a recursive algorithm with a simple computational procedure is derived for the optimal linear filter. As a significant application of the proposed results, the optimal recursive filtering problem in multi-sensor systems with missing measurements and random delays can be addressed. Numerical simulation examples are used to demonstrate the feasibility of the proposed filtering algorithm, which is also compared with other filters that have been proposed.
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