MAD Robust Fusion with Non-Gaussian Channel Noise

Abstract

To solve the problem of distributed multisensor fusion, the optimal linear methods can be used in Gaussian noise models. In practice, channel noise distributions are usually non-Gaussian, possibly heavy-tailed, making linear methods fail. By combining a classical tool of optimal linear fusion and a robust statistical method, the two-stage MAD robust fusion (MADRF) algorithm is proposed. It effectively performs both in symmetrically and asymmetrically contaminated Gaussian channel noise with contamination parameters varying over a wide range.