The great majority of tracking filters that have been developed for intercept applications are essentially variations of the extended Kalman filter. There are two factors that severely degrade the performance of such filters: low observability and modeling errors. The modeling errors may be divided into two categories: errors due to the linearization of nonlinearities and errors due to the unknown target dynamics. The low observability combined with the unknown target maneuver invariably results in state estimates that are corrupted by substantial estimation errors. The linearization around these estimates causes the sensitivity problems which are typical for extended Kalman filters. Because the information contents of the measurements cannot be influenced by the designer of a filter the only way to improve the tracking performance is to increase the robustness of the tracking algorithm with respect to the modeling errors. In this paper a new tracking filter with enhanced robustness is presented. It combines nonlinear estimation techniques with limited memory filtering and time scale separation.
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