Journal
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
Volume 44, Issue 7, Pages 1233-1244Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207721.2012.659709
Keywords
least-squares estimation; randomly delayed observations; covariance information; innovation approach; multiple sensors
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Funding
- Ministerio de Educacion y Ciencia [MTM2011-24718]
- Junta de Andalucia [P07-FQM-02701]
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This article is concerned with the least-squares (LS) linear estimation problem of discrete-time signals from noisy measurements coming from multiple randomly delayed sensors with different delay characteristics. It is assumed that the Bernoulli random variables characterising the measurement delays are correlated at consecutive sampling times. Using an innovation approach, recursive linear filtering and smoothing (fixed-point and fixed-interval) algorithms are obtained without requiring the state-space model generating the signal, but only the covariance functions of the signal and the noise, the delay probabilities and the correlation function of the Bernoulli variables. Also, recursive formulas for filtering and fixed-point smoothing error covariance matrices are obtained to measure the goodness of the proposed estimators.
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