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A new data association algorithm using probability hypothesis density filter
Authors:Zhipei Huang  Shuyan Sun  Jiankang Wu
Affiliation:1. School of Information Science and Engineering,Graduate University of Chinese Academy of Sciences,Beijing 100190,China;Institute of Electronics,Chinese Academy of Sciences,Beijing 100190,China
2. School of Information Science and Engineering,Graduate University of Chinese Academy of Sciences,Beijing 100190,China
Abstract: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 computationally 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.
Keywords:Multi-target trajectory tracking  Probability Hypothesis Density (PHD)  Gaussian mixture (GM) model  Multiple hypotheses detection  Peak-to-track association
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