  Multitarget tracking (MTT) is a challenging task that aims at estimating the
number of targets and their states from measurements of the target states
provided by one or multiple sensors. Additional information, such as imperfect
estimates of target classes provided by a classifier, can facilitate the
target-measurement association and thus improve MTT performance. In this
letter, we describe how a recently proposed MTT framework based on the
sum-product algorithm can be extended to efficiently exploit class information.
The effectiveness of the proposed approach is demonstrated by simulation
results.
