Commenced in January 2007
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Alternative to M-Estimates in Multisensor Data Fusion

Authors: Nga-Viet Nguyen, Georgy Shevlyakov, Vladimir Shin


To solve the problem of multisensor data fusion under non-Gaussian channel noise. The advanced M-estimates are known to be robust solution while trading off some accuracy. In order to improve the estimation accuracy while still maintaining the equivalent robustness, a two-stage robust fusion algorithm is proposed using preliminary rejection of outliers then an optimal linear fusion. The numerical experiments show that the proposed algorithm is equivalent to the M-estimates in the case of uncorrelated local estimates and significantly outperforms the M-estimates when local estimates are correlated.

Keywords: Robustness, estimation, Data fusion, M-estimates

Digital Object Identifier (DOI):

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