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Evidence combination based on prospect theory for multi-sensor data fusion.

Fuyuan Xiao1

  • 1School of Computer and Information Science, Southwest University, Chongqing, 400715, China.

ISA Transactions
|July 6, 2020
PubMed
Summary

This study introduces a novel hybrid multi-sensor data fusion method. It enhances system performance by effectively managing uncertain and conflicting evidence using prospect theory and evidence theory.

Keywords:
Dempster–Shafer evidence theoryEvidence conflictEvidential credibility prospect value functionFault diagnosisMulti-sensor data fusionProspect theoryRecognition

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Area of Science:

  • Engineering
  • Computer Science
  • Information Science

Background:

  • Multi-sensor data fusion (MSDF) enhances system performance but faces challenges with uncertain and conflicting data.
  • Evidence theory effectively manages uncertainty but can yield counterintuitive results with conflicting evidence using Dempster's Combination Rule (DCR).

Purpose of the Study:

  • To develop a hybrid MSDF method that addresses the limitations of traditional evidence theory in handling conflicting data.
  • To improve the accuracy and reliability of data fusion by introducing a novel evidential credibility measure.

Main Methods:

  • A hybrid MSDF approach integrating prospect theory with evidence theory was developed.
  • Key concepts including local/global credibility, credibility estimation, and prospect value functions were defined to assess evidence quality.
  • Weights were assigned to evidences based on their credibility, and primitive evidences were amended before applying DCR.

Main Results:

  • The proposed method effectively measures evidential credibility, assigning appropriate weights to different sensor data.
  • Amending primitive evidences based on credibility weights significantly improved the handling of conflicting data.
  • Experimental results demonstrated the hybrid MSDF approach's superiority over traditional methods in data fusion.

Conclusions:

  • The developed hybrid MSDF method offers a robust solution for managing uncertainty and conflict in multi-sensor systems.
  • This approach enhances the reliability and accuracy of data fusion, particularly in scenarios with contradictory evidence.
  • The integration of prospect theory provides a more nuanced way to evaluate and utilize sensor data.