Factor graph aided multiple hypothesis tracking

Abstract

Since closely moving targets exist extensively in the ground moving target tracking, the uncertainty of data association greatly increases making the measurement-to-track association more difficult. Especially, traditional multiple hypothesis tracking (MHT) has high false tracking rate and track swap. This paper first investigates the measurement based factor graph in data association, and gives the corresponding message passing algorithm. Then, a factor graph aided multiple hypothesis tracking (FGA-MHT) method is proposed, which introduces factor graph based m-best hypothesis producing technique and exploits factor graph based probability refinement algorithm to reduce the uncertainty of measurement-to-track association. Experiment results demonstrate that FGA-MHT reduces times of track swap and increases the correct data association rate in closely moving target tracking.