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A Student's t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking with Outliers.

Zhuowei Liu1, Shuxin Chen2, Hao Wu3

  • 1Information and Navigation College Air Force Engineering University, Xi'an 710077, China. lzwlovef1@163.com.

Sensors (Basel, Switzerland)
|April 5, 2018
PubMed
Summary

The novel Student's t mixture PHD (STM-PHD) filter effectively handles outliers in multi-target tracking. This approach improves tracking accuracy by modeling heavy-tailed noise using Student's t distributions.

Keywords:
PHD filterStudent’s t mixturemulti-target trackingoutliersrobustness

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Area of Science:

  • Robotics and Automation
  • Signal Processing
  • Statistical Inference

Background:

  • Multi-target tracking is crucial for various applications.
  • Probability Hypothesis Density (PHD) filters are widely used but sensitive to outliers.
  • Process and measurement noise outliers degrade PHD filter performance.

Purpose of the Study:

  • To develop a robust Probability Hypothesis Density (PHD) filter resistant to outliers.
  • To enhance multi-target tracking accuracy in noisy environments.

Main Methods:

  • Proposed a novel Student's t mixture PHD (STM-PHD) filter.
  • Modeled heavy-tailed process and measurement noise using Student's t distributions.
  • Approximated multi-target intensity as a mixture of Student's t components.
  • Derived a closed PHD recursion based on Student's t approximation.

Main Results:

  • The STM-PHD filter effectively models heavy-tailed noise.
  • The filter demonstrates resilience against outliers in both process and measurement noise.
  • Simulation results confirm improved tracking accuracy compared to standard PHD filters.

Conclusions:

  • The STM-PHD filter offers a robust solution for multi-target tracking with outliers.
  • This method leverages Student's t distribution properties to handle heavy-tailed noise.
  • The proposed filter maintains good tracking performance even with simultaneous process and measurement outliers.