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Derivation of linear estimation algorithms from measurements affected by multiplicative and additive noises

✍ Scribed by M.J. García-Ligero; A. Hermoso-Carazo; J. Linares-Pérez; S. Nakamori

Book ID: 104007046
Publisher: Elsevier Science
Year: 2010
Tongue: English
Weight: 850 KB
Volume: 234
Category: Article
ISSN: 0377-0427
DOI: 10.1016/j.cam.2010.01.043

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✦ Synopsis

This paper addresses the problem of estimating signals from observation models with multiplicative and additive noises. Assuming that the state-space model is unknown, the multiplicative noise is non-white and the signal and additive noise are correlated, recursive algorithms are derived for the least-squares linear filter and fixed-point smoother. The proposed algorithms are obtained using an innovation approach and taking into account the information provided by the covariance functions of the process involved.

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