Application of Robust Filtering and Smoothing to Tracking Data.

Robust methods provide a fresh approach to the treatment of outliers or wild observations in filtering and smoothing applicatons. The robust M-estimates of regression are extended to filtering and fixed lag smoothing by employing a pseudo density of the observatons in the derivations of the filter and fixed lag smoother. These robust methods are applied to tracking data to obtain improved estimation performance in the presence of wild observations. The improvement in estimaton performance is evaluated via Monte Carlo using simulated tracking data. Author