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Minimax Robust Optimal Estimation Fusion for Distributed Multisensor Systems with a Relative Entropy Uncertainty

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  • Hua Li
  • Jie Zhou

Abstract

This paper considers the robust estimation fusion problem for distributed multisensor systems with uncertain correlations of local estimation errors. For an uncertain class characterized by the Kullback-Leibler (KL) divergence from the actual model to nominal model of local estimation error covariance, the robust estimation fusion problem is formulated to find a linear minimum variance unbiased estimator for the least favorable model. It is proved that the optimal fuser under nominal correlation model is robust while the estimation error has a relative entropy uncertainty.

Suggested Citation

  • Hua Li & Jie Zhou, 2014. "Minimax Robust Optimal Estimation Fusion for Distributed Multisensor Systems with a Relative Entropy Uncertainty," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-6, May.
    • Handle: RePEc:hin:jnlmpe:910971
    DOI: 10.1155/2014/910971
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