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Multisensor Multi-Target Tracking Based on GM-PHD Using Out-Of-Sequence Measurements.

Meiqin Liu1,2, Tianyi Huai3, Ronghao Zheng4

  • 1State Key Laboratory of Industrial Control Technology, Hangzhou 310027, China. liumeiqin@zju.edu.cn.

Sensors (Basel, Switzerland)
|October 9, 2019
PubMed
Summary

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This study enhances multi-target tracking by effectively utilizing out-of-sequence measurements (OOSM). The proposed algorithm improves tracking performance compared to traditional methods using in-sequence measurements.

Area of Science:

  • Multi-target tracking systems
  • Data processing and algorithms

Background:

  • Out-of-sequence measurements (OOSM) are common in tracking systems and can degrade performance if not handled properly.
  • Effective utilization of OOSM is crucial for improving overall tracking accuracy.

Purpose of the Study:

  • To formulate the OOSM problem in multi-target tracking.
  • To enhance tracking performance by developing a method to fully utilize OOSM.

Main Methods:

  • Formulation of the OOSM problem.
  • Introduction of the random finite set (RFS)-based Gaussian mixture probability hypothesis density (GM-PHD) filter.
  • Derivation of equations for re-updating posterior intensity with OOSM.
  • Implementation of the GM-PHD filter incorporating OOSM.
Keywords:
out-of-sequence, multi-target tracking, random finite set, gaussian mixture probability hypothesis density

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Main Results:

  • The proposed GM-PHD algorithm effectively utilizes OOSM.
  • Simulations demonstrate superior tracking performance of the GM-PHD with OOSM compared to using in-sequence measurements (ISM) only.

Conclusions:

  • The developed algorithm significantly improves multi-target tracking performance by leveraging OOSM.
  • The findings validate the effectiveness of the proposed approach for handling OOSM in practical tracking scenarios.