  To account for joint tracking and classification (JTC) of multiple targets
from observation sets in presence of detection uncertainty, noise and clutter,
this paper develops a new trajectory probability hypothesis density (TPHD)
filter, which is referred to as the JTC-TPHD filter. The JTC-TPHD filter
classifies different targets based on their motion models and each target is
assigned with multiple class hypotheses. By using this strategy, we can not
only obtain the category information of the targets, but also a more accurate
trajectory estimation than the traditional TPHD filter. The JTC-TPHD filter is
derived by finding the best Poisson posterior approximation over trajectories
on an augmented state space using the Kullback-Leibler divergence (KLD)
minimization. The Gaussian mixture is adopted for the implementation, which is
referred to as the GM-JTC-TPHD filter. The L-scan approximation is also
presented for the GM-JTC-TPHD filter, which possesses lower computational
burden. Simulation results show that the GM-JTC-TPHD filter can classify
targets correctly and obtain accurate trajectory estimation.

Signal Processing