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An improved ET-GM-PHD filter for multiple closely-spaced extended target tracking

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Abstract

This paper presents an enhanced version of the ET-GM-PHD algorithm, a recently developed multiple extended target tracking (METT) technique. The original ET-GM-PHD filter tends to underestimate the target number, because the likelihood estimate in the state update process may poorly approximate the real one when targets are close to each other. The proposed algorithm addresses this drawback via introducing a new penalty strategy in estimating the measurement likelihood. Besides, Gaussian component labeling technique is adopted to obtain individual target tracks. Simulations show that for closely-spaced extended target tracking, the improved method achieves track continuity and exhibits better estimation accuracy over the original ET-GM-PHD filter.

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Authors and Affiliations

  1. School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, China

    Jinlong Yang, Peng Li, Le Yang & Hongwei Ge

Authors
  1. Jinlong Yang
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  2. Peng Li
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  3. Le Yang
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  4. Hongwei Ge
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Corresponding author

Correspondence to Jinlong Yang.

Additional information

Recommended by Associate Editor Young Soo Suh under the direction of Editor Duk-Sun Shim. This work was supported by the National Natural Science Foundation of China (No. 61305017 and No. 61304264) and the Natural Science Foundation of Jiangsu Province (No. BK20130154).

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Yang, J., Li, P., Yang, L. et al. An improved ET-GM-PHD filter for multiple closely-spaced extended target tracking. Int. J. Control Autom. Syst. 15, 468–472 (2017). https://doi.org/10.1007/s12555-015-0193-x

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  • DOI: https://doi.org/10.1007/s12555-015-0193-x

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