Robust fusion of location information

A sensor fusion problem for location data using statistical decision theory (SDT) is studied. The contribution of this study is the application of SDT to obtain a robust test of the hypothesis that data from different sensors is consistent and a robust procedure for combining the date which pass this preliminary consistency test. Here, robustness refers to the statistical effectiveness of the decision rules when the probability distributions of the observation noise and the a priori position information associated with the individual sensors are uncertain. Location data refers to observations of the form Z= theta +V, where V represents additive sensor noise and theta denotes the sensed parameter of interest to the observer. The paper focuses on epsilon -contamination models, which allow one to account for heavy-tailed deviations from nominal sampling distributions.<>