Highly maneuverable target tracking based on efficient propagation of model hypotheses

This paper presents an algorithm based on the multiple model approach for tracking highly maneuverable targets. The proposed method aims at propagating a low number of model hypotheses while still yielding low estimation errors compared to other multiple model algorithms. Merging and pruning techniques are used to keep very small sets of model sequences matched with the target dynamics. Simulation results show that the proposed method is able to attain superior performance during periods of no maneuver compared to the Interacting Multiple Model algorithm as well as when abrupt changes take place in the target movement.