Probabilistic data association filter

<p>The probabilistic data association filter (PDAF) is a statistical approach to the problem of <a href="/facts/Radar_tracker/Jg3h9pWq">plot association</a> (target-measurement assignment) in a target tracking algorithm. Rather than choosing the most likely assignment of measurements to a target (or declaring the target not detected or a measurement to be a false alarm), the PDAF takes an <a href="/facts/Expected_value/1XV0JKL8">expected value</a>, which is the <a href="/facts/Minimum_mean_square_error/hI2lqEMh">minimum mean square error</a> (MMSE) estimate. The PDAF on its own does not confirm nor terminate tracks.
</p><p>Whereas the PDAF is designed to track only one target in the presence of false alarms and missed detections, the <a href="/facts/Joint_Probabilistic_Data_Association_Filter/p17YpQPQ">Joint Probabilistic Data Association Filter</a> (JPDAF) can handle multiple targets. The first real-world application of the PDAF was probably in the <a href="/facts/Jindalee_Operational_Radar_Network/bJ8ekRpN">Jindalee Operational Radar Network</a>, which is an Australian <a href="/facts/Over-the-horizon_radar/o1yno6AP">over-the-horizon radar</a> (OTHR) network.
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Probabilistic data association filter open-in-new

Probabilistic data association filter