期刊
DIGITAL SIGNAL PROCESSING
卷 20, 期 2, 页码 528-540出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.dsp.2009.06.011
关键词
Least-squares estimation; Randomly delayed observations; Covariance information; Innovation approach
资金
- Ministerio de Educacion y Ciencia
- Junta de Andalucia [MTM2008-05567, P07-FQM-02701]
Recursive filtering and smoothing algorithms to estimate a signal from noisy measurements coming from multiple randomly delayed sensors, with different delay characteristics, are proposed. To design these algorithms an innovation approach is used, assuming that the state-space model of the signal is unknown and using only covariance information. To measure the precision of the proposed estimators formulas to calculate the filtering and smoothing error covariance matrices are also derived. The effectiveness of the estimators is illustrated by a numerical simulation example where a signal is estimated using observations from two randomly delayed sensors having different delay properties. (C) 2009 Elsevier Inc. All rights reserved.
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