Multiple model adaptive estimation is investigated as a means of changing the field-of-view as well as the bandwidth of an infrared image tracker against a wide dynamic range of targets. The multiple models are created by tuning the filters for best performance at differing conditions of exhibited target behavior and differing the physical size of their respective fields of view, and 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, whereas an extended Kalman filter, processing the raw infrared data directly and assuming a nonlinear constant turn-rate target dynamics model provides superior tracking capability, especially for harsh maneuvres.
Adaptive field of view expansion via multiple model filtering for tracking dynamic target images
Angepasste Erweiterung des Sichtfeldes durch mehrfache Modellfilterung zur Verfolgung von dynamischen Zielbildern
1985
10 Seiten, 8 Bilder, 2 Tabellen, 34 Quellen
Aufsatz (Konferenz)
Englisch
Target Tracking by Multiple Particle Filtering
IEEE | 2007
|British Library Online Contents | 2003
|Multiple structure adaptive target tracking
Tema Archiv | 1990
|