Enhanced Multiple Model Tracker Based on Gaussian Mixture Reduction for a Maneuvering Target in Clutter

摘要

Multiple hypothesis trackers (MHTs) are widely accepted as the best means of tracking targets in the presence of clutter. This research seeks to incorporate multiple model Kalman filters into an Integral Square Error (ISE) cost-function-based MHT to increase the fidelity of target state estimation. Results indicate that the proposed multiple model methods can properly identify the maneuver mode of a target in dense clutter and ensure that an appropriately tuned filter is used. During benign portions of flight, this causes significant reductions in position and velocity RMS errors compared to a single-dynamics-model-based MHT. During portions of flight when the mixture mean deviates significantly from true target position, so-called deferred decision periods, the multiple model structures tend to accumulate greater RMS errors than a single-dynamics-model-based MHT, but this effect is inconsequential considering the inherently large magnitude of these errors (a non-MHT tracker would not be able to track during these periods at all). The multiple model MHT structures do not negatively impact track life when compared to a single-dynamics-model-based MHT.