Multiple Detection Probabilistic Data Association filter for multistatic target tracking

Abstract—A standard assumption in most tracking algorithms, like the Probabilistic Data Association (PDA) filter, Multiple Hy-pothesis Tracker (MHT) or the Multiframe Assignment Tracker (MFA), is that a target is detected at most once in a frame of data used for association. This one-to-one assumption is essential for correct measurement-to-track associations. When this assumption is violated, the above algorithms treat the extra detections as random clutter. When multiple detections from the same target fall within the association gate, the PDA filter tries to apportion the association probabilities, but with the fundamental assumption only one of them is correct. The MFA and the MHT algorithms try to spawn multiple tracks to handle the additional measurements from the same target, assuming at most one measurement came from each target. Both of these approaches have undesirable side effects since they ignore the possibility of multiple detections from the same target in a scan of data. Such multiple detection situations occur in multistatic tracking problems. In this paper, we proposed a new Multiple Detection Proba-bilistic Data Association (MD-PDA) filter for tracking a target when more than one target originated measurement may exist within the validation gate. In the proposed MD-PDA, combina-torial association events are formed to handle the possibility of multiple measurements from the same target. Modified associa-tion probabilities are calculated with the explicit assumption of multiple detections. Simulations are presented to demonstrate the effectiveness of the algorithm on a single target tracking problem in clutter. Extensions to handle multiple targets using the Joint PDA, MHT and MFA approaches are under development.

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