Cubature Information Gaussian Mixture Probability Hypothesis Density Approach for Multi Extended Target Tracking

@article{Liu2019CubatureIG,
  title={Cubature Information Gaussian Mixture Probability Hypothesis Density Approach for Multi Extended Target Tracking},
  author={Zhe Liu and Linna Ji and Fengbao Yang and Xiqiang Qu and Zhiliang Yang and Dongze Qin},
  journal={IEEE Access},
  year={2019},
  volume={7},
  pages={103678-103692}
}
In multi-extended target tracking, each target may generate more than one observation. The traditional probability hypothesis density (PHD)-based methods are no longer effective in such scenarios. Recently, the Gaussian mixture PHD approach for the extended target tracking (ET-GM-PHD) has been presented to solve such a problem. The tracking performance of this approach has been restricted by the following disadvantages. First, it only focuses on the linear models. When targets are moving with… CONTINUE READING

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