Track-to-track association for tracks with features and attributes

The problem of track-to-track association - a prerequisite for fusion of tracks - has been considered in the literature only for tracks described by kinematic states. The association of tracks from a common target can also be solved using additional feature or attribute variables which are associated with those tracks. We extend the existing results to the situation where track association is done using feature variables, which are continuous valued, as well as target classification information or attributes, which are discrete valued. The sufficient statistic for the optimal association test (in the Neyman-Pearson sense) based on discrete-valued target classification information observables (attributes) is derived and its relationship with the class probability vector is discussed. Based on this, "attribute gates" are presented, which play a similar role to the kinematic gates in track-to-track association.