Adaptive Tracker Field-of-View Variation Via Multiple Model Filtering

Abstract
Adaptive estimation using multiple model filtering is investigated as a means of changing the field of view as well as the bandwidth of an infrared image tracker when target acceleration can vary over a wide range. The multiple models are created by tuning filters for best performance at differing conditions of exhibited target behavior and differing physical size of their respective fields of view. Probabilistically weighted averaging provides the adaptation mechanism. Each filter involves online identification of the target shape function, so that this algorithm can be used against ill-defined and/or multiple-hot-spot targets. When each individual filter has the form of an enhanced correlator/linear Kalman filter, computational loading is very low. In contrast, an extended Kalman filter processing the raw infrared data directly and assuming a nonlinear constant turn-rate dynamics model provides superior tracking capability, especially for harsh maneuvers, at the cost of a larger computational burden.

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