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A Networked Method for Multi-Evidence-Based Information Fusion.

Qian Liang1, Zhongxin Liu1, Zengqiang Chen1

  • 1College of Artificial Intelligence, Nankai University, No. 38 Tongyan Road, Jinnan District, Tianjin 300350, China.

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Summary
This summary is machine-generated.

This study introduces a new method for Dempster-Shafer evidence theory to improve multi-sensor data fusion. By evaluating evidence credibility and using an information interaction graph, it enhances fusion accuracy, especially with conflicting sensor data.

Keywords:
Dempster–Shafer theorydata fusionevidence interactionevidential conflictsensor malfunction

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Area of Science:

  • Artificial Intelligence
  • Data Fusion
  • Sensor Networks

Background:

  • Dempster-Shafer evidence theory is effective for multi-sensor data fusion.
  • Existing methods struggle with highly conflicting evidence, particularly from sensor malfunctions.
  • Improved combination rules exist but have limitations.

Purpose of the Study:

  • To present a novel combination method integrating information interaction graphs with Dempster-Shafer evidence theory.
  • To address deficiencies in current fusion methods when dealing with sensor malfunction-induced conflicts.
  • To enhance the accuracy of multi-sensor data fusion.

Main Methods:

  • Representing the multi-evidence fusion process as a network using an information interaction graph.
  • Calculating the credibility of each piece of evidence by measuring inter-evidence distance.
  • Evaluating evidence credibility and excluding unreliable evidence from the network.
  • Fusing connected and credible evidence within the network structure.

Main Results:

  • The proposed method effectively filters unreliable evidence by assessing credibility.
  • Integrating evidence into a network structure improves fusion accuracy.
  • Application results demonstrate the method's effectiveness in handling conflicting sensor data.

Conclusions:

  • The presented approach enhances Dempster-Shafer evidence theory for multi-sensor data fusion.
  • The information interaction graph effectively models evidence relationships and credibility.
  • The method offers improved accuracy and reliability in fusion, particularly under sensor malfunction conditions.