Substanitial enhancements to a multiple model adaptive estimator for target image tracking

Enhancements to a previously devised multiple model adaptive estimator for target image tracking are developed and analyzed: (1) allowing some of the elemental filters to have rectangular fields-of-view and to be tuned for target dynamics that are harsher in one direction than others, (2) considering both Gauss-Markov acceleration models and constant turn-rate models for target dynamics, (3) tuning dynamics noise strengths as a function of sensor-to-target range, and (4) devising an initial target acquisition algorithm to remove important biases in the estimated target template to be used in a correlator within the tracker. Particularly good adaptation responsiveness is demonstrated in the multiple model algorithm´s ability to handle harsh maneuver onset, yielding performance essentially equivalent to that of the best artificially informed tracking algorithm.