The joint probabilistic data-association filter (JPDAF)[1] is a statistical approach to the problem of plot association (target-measurement assignment) in a target tracking algorithm.
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 common problem observed with the JPDAF is that estimates of closely spaced targets tend to coalesce over time.
[3][4] This is because the MMSE estimate is typically undesirable when target identity is uncertain.