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

الملخص EN

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.