Multiple Hypotheses Detection with Gaussian Mixture Probability Hypothesis Density Filter for Multi-target Trajectory Tracking
多元假设检验GMPHD轨迹跟踪

Multi-target tracking is becoming one of most focusing research topics because of the modern military affair requirements as well as civil developments. Among all the techniques, Probability Hypothesis Density(PHD) filtering approach, especially Gaussian Mixture PHD(GMPHD) filter, which has a closed form recursion, has shown its advantages in tracking unknown number of targets despite the impact of noise and missing detection etc. Existing PHD trajectory tracking methods combining PHD filter, which can not estimate the trajectories of multi-target alone, with traditional data association, are computationally expensive and almost intractable. In this paper, a brand new multi-target trajectory tracking algorithm based on random finite set theory is brought forward by adopting classical signal detection technique along with GMPHD filter. Using hypotheses representing the trajectory information, data association is accomplished through the hypothesis matrix judging on the same basement as track managing function. The simulation results suggest that this algorithm not only significantly alleviates the heavy computing load, but also performs multi-target trajectory tracking effectively in the meantime.