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Multi-sensor data fusion based on soft likelihood functions and OWA aggregation and its application in target

Xiangjun Mi1, Tongxuan Lv1, Ye Tian1

  • 1College of Information Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China.

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|December 22, 2020
PubMed
Summary

This study introduces a hybrid method for Dempster-Shafer theory (DST) to improve multi-sensor data fusion with conflicting evidence. The new approach enhances fusion credibility and accuracy, particularly in target recognition tasks.

Keywords:
Dempster–Shafer theoryMulti-sensor data fusionOrdered weighted averaging aggregationSoft likelihood functionsTarget recognition system

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Area of Science:

  • Information Fusion
  • Artificial Intelligence
  • Decision Theory

Background:

  • Multi-sensor data fusion is critical in real-world applications.
  • Dempster-Shafer theory (DST) effectively handles uncertain information but struggles with highly contradictory evidence.
  • Existing DST methods can yield counterintuitive results when evidence conflicts.

Purpose of the Study:

  • To propose a novel hybrid method for combining belief functions within DST.
  • To address the limitations of DST in handling highly contradictory evidence.
  • To enhance the comprehensiveness and credibility of multi-sensor data fusion results.

Main Methods:

  • A hybrid approach combining soft likelihood functions (SLFs) with ordered weighted averaging (OWA) operators.
  • Utilizing SLFs based on OWA operators to fuse compatible uncertain information.
  • Characterizing the impact of unknown uncertain factors on probability information in evidence.

Main Results:

  • The proposed method demonstrates reliability in fusing uncertain information from multiple sources.
  • Experimental results show significant advantages in solving conflict evidence fusion problems in multi-sensor systems.
  • Achieved a 96.92% target recognition rate when fusing three pieces of evidence in a specific application.

Conclusions:

  • The hybrid method offers a more comprehensive and credible approach to DST-based data fusion.
  • This technique effectively resolves issues arising from conflicting evidence in multi-sensor environments.
  • The approach shows practical advantages, particularly in applications like target recognition.