Target tracking with retrodicted discrete probabilities
Oliver E. Drummond · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997
The concept of retrodiction of discrete probabilities is exploited in this paper to provide alternative data association algorithms for tracking multiple targets with a single or multiple sensors. These algorithms employ multiple frames of data in the data association processing. The concept of retrodiction is also applied to the task of multiple model filtering. Alterative optimization criteria are also exploited to provide alternative association approaches and each approach is expected to exhibit different estimation error characteristics. These additional association approaches provide wider selection to better tailor the tracking algorithms to a specific application. These approaches offer improved performance over single-frame association tracking approaches. This improved performance is obtained, however, at the expense of increased processing load. A number of different approaches are described that employ multiple-frame data association. Similarly, a number of different approaches are also described that employ a moving window of multiple measurements for multiple model filtering. With these algorithms, design parameters can be selected to adjust performance to suit a specific application.