Robust bootstrap based observation classification for Kalman Filtering in harsh LOS/NLOS environments

The bootstrap allows for the estimation of the distribution of an estimate without requiring assumptions on the distribution of the underlying data, relying on asymptotic results or theoretical derivations. In contrast to a point estimate, the distribution estimate captures the uncertainty about the statistic of interest. We introduce a novel robust bootstrap method and demonstrate how this additional information is utilized to improve the performance of robust tracking methods. A robust bootstrap method is crucial, because the classical bootstrap is highly sensitive to outliers, irrespective of the robustness of the underlying estimator. Using the robust distribution estimate of the state prediction as a measure of confidence, the bootstrap allows to incorporate an observation weighting scheme into the tracking algorithm, which enhances performance.