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Measurement based dimension descent association algorithm for OTHR multi-detection multi-target tracking.

Yujie Zhang1, Zheng Zhang1, Jianglong Yu1

  • 1School of Automation Science and Electrical Engineering, Science and technology on Aircraft Control Laboratory, Beihang University, China.

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|June 10, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a dimension descent association (DDA) algorithm to efficiently handle multi-detection multi-target tracking (MDMTT) challenges in dense clutter. The DDA algorithm reduces computational load for 3D data association, improving tracking performance.

Keywords:
Convergence analysisDimension descent associationMulti-detection multi-target trackingTarget detection

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Area of Science:

  • Radar Systems Engineering
  • Signal Processing
  • Data Association Algorithms

Background:

  • Multi-detection multi-target tracking (MDMTT) in dense clutter environments presents significant challenges.
  • The primary difficulty lies in 3-dimensional multipath data association among measurements, detection models, and targets.
  • Dense clutter environments generate numerous false measurements, increasing computational burden for traditional 3D data association.

Purpose of the Study:

  • To address the computational complexity of 3D multipath data association in MDMTT within dense clutter.
  • To propose an efficient algorithm for handling complex data association problems in over-the-horizon radar systems.
  • To introduce a method for detecting newly appearing targets (new-born targets) using sequential measurements.

Main Methods:

  • A measurement-based dimension descent association (DDA) algorithm is proposed.
  • The DDA algorithm decomposes the 3D multipath data association into two 2D data associations.
  • A time extension method is developed for detecting new-born targets based on sequential measurements.

Main Results:

  • The proposed DDA algorithm significantly reduces computational burden compared to optimal 3D multipath data association.
  • Computational complexity analysis demonstrates the efficiency of the DDA algorithm.
  • Simulations confirm the effectiveness and rapidity of the measurement-based DDA algorithm against existing methods.

Conclusions:

  • The measurement-based DDA algorithm provides an effective solution for 3D multipath data association in MDMTT.
  • The algorithm offers reduced computational complexity, making it suitable for dense clutter environments.
  • The convergence analysis shows that estimation error approaches zero with an increasing number of Gaussian mixtures.