  Forward-backward Probability Hypothesis Density (PHD) smoothing is an
efficient way for target tracking in dense clutter environment. Although the
target class has been widely viewed as useful information to enhance the target
tracking, there is no existing work in literature which incorporates the
feature information into PHD smoothing. In this paper, we generalized the PHD
smoothing by extending the general mode, which includes kinematic mode, class
mode or their combinations etc., to forward-backward PHD filter. Through a
top-down method, the general mode augmented forward-backward PHD smoothing is
derived. The evaluation results show that our approach out-performs the
state-of-art joint detection, tracking and classification algorithm in target
state estimation, number estimation and classification. The reduction of OSPA
distance is up to 40%.
