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A new data association algorithm using probability hypothesis density filter

  • Published:
Journal of Electronics (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.

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Authors and Affiliations

  1. School of Information Science and Engineering, Graduate University of Chinese Academy of Sciences, Beijing, 100190, China

    Zhipei Huang, Shuyan Sun & Jiankang Wu

  2. Institute of Electronics, Chinese Academy of Sciences, Beijing, 100190, China

    Zhipei Huang

  3. Room 509, Moshi Building, No. 95, Zhongguancun East Rd., Beijing, 100190, China

    Zhipei Huang

Authors
  1. Zhipei Huang
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  2. Shuyan Sun
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  3. Jiankang Wu
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Corresponding author

Correspondence to Zhipei Huang.

Additional information

Supported by the National Natural Science Foundation of China (No. 60772154) and the President Foundation of Graduate University of Chinese Academy of Sciences (No. 085102GN00).

Communication author: Huang Zhipei, born in 1973, female, Assistant Professor.

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Cite this article

Huang, Z., Sun, S. & Wu, J. A new data association algorithm using probability hypothesis density filter. J. Electron.(China) 27, 218–223 (2010). https://doi.org/10.1007/s11767-010-0304-0

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  • DOI: https://doi.org/10.1007/s11767-010-0304-0

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