Multiple target tracking with multiple frame, probabilistic data association

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Abstract
Probabilistic data association approaches are described for tracking multiple targets. These approaches employ multiple frames of data in the data association processing. These approaches offer improved performance over Joint Probabilistic Data Association tracking. This improved performance is obtained, however, at the expense of increased processing load. In the algorithms are design parameters that can be selected to adjust to suit a specific application. The concept of retrodicted hypothesis probability is introduced. Retrodicted hypothesis probabilities are used in an effort to better approximate optimal tracking. Some of these algorithms are retrospective in that, as each new frame of sensor data becomes available, earlier tracks are modified and these changes impact subsequent tracks.

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