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Probability Hypothesis Density (PHD) filtering approach has shown its advantages in tracking time varying number of targets even when there are noise,clutter and misdetection. For linear Gaussian Mixture (GM) system,PHD filter has a closed form recursion (GMPHD). But PHD filter cannot estimate the trajectories of multi-target because it only provides identity-free estimate of target states. Existing data association methods still remain a big challenge mostly because they are com-putationally expensive. In this paper,we proposed a new data association algorithm using GMPHD filter,which significantly alleviated the heavy computing load and performed multi-target trajectory tracking effectively in the meantime.
Probability Hypothesis Density (PHD) filtering approach has shown its advantages in tracking time varying number of targets even when there are noise, clutter and misdetection. For Linear Gaussian Mixture (GM) system, PHD filter has a closed form recursion (GMPHD). But PHD filter can not estimate the trajectories of multi-target because it only provides identity-free estimate of target states. Existing this paper association methods still remain a big challenge mostly because they are com-putationally expensive. In this paper, we propose a new data association algorithm using GMPHD filter, which significantly alleviated the heavy computing load and performed multi-target trajectory tracking effectively in the meantime.