A new data association algorithm using probability hypothesis density filter |
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Authors: | Zhipei Huang Shuyan Sun Jiankang Wu |
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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 |
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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. |
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Keywords: | Multi-target trajectory tracking Probability Hypothesis Density (PHD) Gaussian mixture (GM) model Multiple hypotheses detection Peak-to-track association |
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