Probabilistic data association filter

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The Probabilistic Data Association Filter (PDAF) [1] [2] is a statistical approach to the problem of plot association (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 expected value, which is the minimum mean square error (MMSE) estimate. The PDAF on its own does not confirm nor terminate tracks.

Whereas the PDAF is only designed to track a single target in the presence of false alarms and missed detections, the Joint Probabilistic Data Association Filter (JPDAF) can handle multiple targets. The first real-world application of the PDAF was probably in the Jindalee Operational Radar Network, [2] which is an Australian over-the-horizon radar (OTHR) network.

Implementations

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The joint probabilistic data-association filter (JPDAF) is a statistical approach to the problem of plot association in a target tracking algorithm. Like the probabilistic data association filter (PDAF), rather than choosing the most likely assignment of measurements to a target, the PDAF takes an expected value, which is the minimum mean square error (MMSE) estimate for the state of each target. At each time, it maintains its estimate of the target state as the mean and covariance matrix of a multivariate normal distribution. However, unlike the PDAF, which is only meant for tracking a single target in the presence of false alarms and missed detections, the JPDAF can handle multiple target tracking scenarios. A derivation of the JPDAF is given in.

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References

  1. Bar-Shalom, Yaakov; Tse, Edison (1975). "Tracking in a Cluttered Environment With Probabilistic Data Association". Automatica. 11 (5): 451–460. doi:10.1016/0005-1098(75)90021-7.
  2. 1 2 Bar-Shalom, Yaakov; Daum, Fred; Huang, Jim (December 2009). "The probabilistic data association filter". IEEE Control Systems Magazine. 29 (6): 82–100. doi:10.1109/MCS.2009.934469. S2CID   6875122.
  3. "Tracker Component Library". Matlab Repository. Retrieved January 5, 2019.
  4. "Stone Soup Github Repo". GitHub .
  5. "Stone Soup PDA Tutorial Documentation".
  6. "Stone Soup PDA Tutorial Code". GitHub .