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Anti-clutter Gaussian Inverse Wishart PHD Filter for Extended Target Tracking.

Yuan Huang1, Liping Wang1, Xueying Wang1

  • 1College of Electronic Science, National University of Defense Technology, Changsha 410073, China.

Sensors (Basel, Switzerland)
|November 28, 2019
PubMed
Summary

This study introduces an anti-clutter filter to improve target tracking. By using hypothesis testing, the filter accurately distinguishes targets from clutter, enhancing the performance of the extended target Gaussian inverse Wishart probability hypothesis density (ET-GIW-PHD) filter.

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Area of Science:

  • Multi-target tracking
  • Probabilistic data association
  • Signal processing

Background:

  • The extended target Gaussian inverse Wishart probability hypothesis density (ET-GIW-PHD) filter often overestimates targets in dense clutter.
  • This inaccuracy stems from difficulties in correctly identifying measurement origins based solely on measurement counts.

Purpose of the Study:

  • To develop an improved anti-clutter filter for multi-target tracking.
  • To enhance the accuracy of the ET-GIW-PHD filter in high-clutter environments.

Main Methods:

  • Proposed an anti-clutter filter incorporating hypothesis testing.
  • Considered measurement counts, target states, and clutter distribution for clutter identification.
  • Deduced target and clutter likelihood functions to derive a likelihood ratio test statistic.
Keywords:
PHD filterextended targethigh clutter densitytarget tracking

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  • Utilized a chi-square distribution and a confidence threshold to classify measurements as clutter.
  • Revised the ET-GIW-PHD filter's correction step based on hypothesis testing outcomes.

    • Main Results:

    • The proposed method effectively distinguishes between target-originated and clutter-originated measurements.
    • The likelihood ratio test statistic follows a chi-square distribution, enabling robust clutter rejection.
    • Experimental results demonstrate significant performance improvements over the standard ET-GIW-PHD filter.

    Conclusions:

    • The novel anti-clutter filter significantly enhances multi-target tracking accuracy in cluttered scenarios.
    • Hypothesis testing provides a reliable mechanism for clutter suppression, improving filter performance.
    • The revised ET-GIW-PHD filter offers a more robust solution for real-world tracking applications.