Noncausal Estimation for Discrete Gauss-Markov Random Fields

  • Bernard C. Levy
Part of the Progress in Systems and Control Theory book series (PSCT, volume 3)

Abstract

In [1], it was shown that 2-D discrete Gauss-Markov random fields can be characterized in terms of a noncausal nearest-neighbor model (NNM) driven by locally correlated noise. This result is used here to obtain a simple solution of the smoothing problem for Gauss-Markov random fields. It is shown that the smoother has a nearest-neighbor structure of the same type as the original field, and that the smoothing error is itself a Gauss-Markov random field. Since the operator describing the smoother dynamics is positive and self-adjoint, the smoother can be implemented by using efficient iterative algorithms for elliptic PDEs.

Keywords

Covariance Resi Acoustics Alan 

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Copyright information

© Birkhäuser Boston 1990

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  • Bernard C. Levy

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