2018/12/06 by Yanyuan Qin, Qin, Yanyuan
Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #Fault Detection and Control Systems #Information Theory (cs.IT) #Infrared Target Detection Methodologies #Systems and Control (eess.SY) #Target Tracking and Data Fusion in Sensor Networks #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1812.02599
openalex publication_date 2018/12/06 · openalex created_date 2018/12/11 · openalex updated_date 2026/08/01
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%.