Multiple hypothesis method for tracking move-stop-move target

Abstract

This study presents a new approach to track the move-stop-move targets. In the presence of the radar Doppler blind area, a move-stop-move target may lead to track breakages, which has some adverse effects on estimating the number of targets in the surveillance area. The authors develop a mode switching-based multiple hypothesis tracking (MHT) method to solve this problem. In each association, MHT generates three types of hypotheses: moving and detected, moving and undetected and stopped. In the first two types of hypotheses, the target is in the mode of move and tracked with interacting multiple model (IMM)-extended Kalman filter (EKF) estimator, while in the third kind of hypotheses, the target is in the mode of stop and tracked by EKF with a stop model. The mode probabilities are calculated using the kinematic state statistics from the estimator and the Markov transfer probability of the target movement stage. The track discontinuity event in the classical log-likelihood ratio of MHT is modified as well. The results of simulation experiment show that the proposed MHT-based tracker outperforms the conventional MHT-based method when tracking move-stop-move target.

Keywords

• filtering theory
• radar tracking
• Doppler radar
• nonlinear filters
• Markov processes
• Kalman filters
• probability
• target tracking
• track discontinuity event
• MHT-based tracker
• conventional MHT-based method
• tracking move-stop-move
• multiple hypothesis method
• move-stop-move targets
• radar Doppler blind area
• track breakages
• surveillance area
• mode switching-based multiple hypothesis tracking method
• multiple model-extended Kalman filter estimator
• stop model
• movement mode
• mode probabilities
• target movement stage