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Consensus-Based Track Association with Multistatic Sensors under a Nested Probabilistic-Numerical Linguistic

Xinxin Wang1, Zeshui Xu2,3, Xunjie Gou4

  • 1Business School, State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610064, China. wangxinxin_cd@163.com.

Sensors (Basel, Switzerland)
|March 23, 2019
PubMed
Summary

This study introduces a novel track association algorithm using multi-attribute group decision-making and a consensus model with nested probabilistic-numerical linguistic term sets. The method effectively handles complex multi-target tracking scenarios.

Keywords:
MAGDMNPNLTSsconsensus modelmulti-static sensorstrack association

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Area of Science:

  • Engineering
  • Computer Science
  • Decision Science

Background:

  • Track association is crucial for multi-target tracking in complex environments.
  • Sensor diversity and maneuvering targets pose significant challenges for accurate track association.
  • Existing methods struggle with consensus-building across multiple sensors.

Purpose of the Study:

  • To develop an automated track association algorithm for multisensor systems.
  • To address challenges in separating tracks from multiple maneuvering targets.
  • To enhance consensus mechanisms in track association.

Main Methods:

  • Transforming track association into multi-attribute group decision-making (MAGDM).
  • Utilizing nested probabilistic-numerical linguistic term sets (NPNLTSs) for MAGDM.
  • Constructing a consensus model with consensus checking and modification processes.
  • Developing an automated track association algorithm based on the consensus model.

Main Results:

  • A practical case study demonstrated the algorithm's ability to obtain corresponding tracks.
  • The proposed method showed effectiveness, feasibility, and applicability in comparisons.
  • A discriminant analysis method was provided for single echo point scenarios.

Conclusions:

  • The developed consensus model and track association algorithm provide effective technical support for multisensor track association problems.
  • The method offers improvements over existing approaches in handling complex tracking environments.
  • The research contributes to advancing automated target recognition and tracking systems.