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Derivation of centralized and distributed filters using covariance information

Author

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  • García-Ligero, M.J.
  • Hermoso-Carazo, A.
  • Linares-Pérez, J.

Abstract

The problem of estimating a degraded image using observations acquired from multiple sensors is addressed when the image degradation is modelled by white multiplicative and additive noise. Assuming the state-space model is unknown, the centralized and distributed filtering algorithms are derived using the information provided by the covariance functions of the processes involved in the measurement equation. The filters obtained are applied to an image affected by multiplicative and additive noise, and the goodness of the centralized and distributed filters is compared by examining the respective filtering error variances.

Suggested Citation

  • García-Ligero, M.J. & Hermoso-Carazo, A. & Linares-Pérez, J., 2011. "Derivation of centralized and distributed filters using covariance information," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 312-323, January.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:1:p:312-323
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    References listed on IDEAS

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    1. Shin, Vladimir & Shevlyakov, Georgy & Kim, Kiseon, 2007. "A new fusion formula and its application to continuous-time linear systems with multisensor environment," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 840-854, October.
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