Tracking, association, and classification: A combined PMHT approach

Abstract

When tracking more than one object, a key problem is that of associating measurements with particular tracks. Recently, powerful statistical approaches such as probabilistic multihypothesis tracking (PMHT) and probabilistic least squares tracking have been proposed to solve the problem of measurement to track association. However, in practice other information may often be available, typically classification measurements from automatic target recognition algorithms, which help associate certain measurements with particular tracks. An extension to the Bayesian PMHT approach which allows noisy classification measurements to be incorporated in the tracking and association process is derived. Some example results are given to illustrate the performance improvement that can result from this approach.