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η-Filter: An Evidence Theoretic Approach to Unmanned Ground Vehicle Localization

In this paper, we present a novel evidence theoretic fusion filler, and its application to the Unmanned Ground Vehicle (UGV) localization problem. The various components of the sensor fusion framework such as the adaptive pre-processing unit, the evidence extraction and combination unit, and the extended Kalman filter are described in detail. The crux of this architecture is the evidence extraction and combination unit that employs a two-pronged approach, one to switch between parametric models, and another to adaptively vary the measurement noise covariance matrix. The process of evidence extraction using fuzzy-type or rule-based techniques, and their subsequent combination using the Dempster's rule for combination are detailed. An experiment is conducted to demonstrate the merits of this UGV localization approach. Finally, we conclude with a brief summary of the results.