The joint probabilistic data association filter (JPDAF) is a statistical approach to the problem of plot association in a radar tracker, in which all of the potential candidates for association to a track are combined in a single statistically most probable update, taking account of the statistical distribution of the track errors and clutter and assuming that more than one of the candidates is a target, and the rest are false alarms. It is an extension of the probabilistic data association filter

Probabilistic data association filter

The probabilistic data association filter is a statistical approach to the problem of plot association in a radar tracker, in which all of the potential candidates for association to a track are combined in a single statistically most probable update, taking account of the statistical distribution...

 which assumes that only one of the potential candidates represents an actual target.

The JPDAF is one of several techniques used for visual target tracking in the field of Computer Vision

Computer vision

Computer vision is a field that includes methods for acquiring, processing, analysing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions...