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A New Evidence Weight Combination and Probability Allocation Method in Multi-Sensor Data Fusion.

Li Ma1,2, Wenlong Yao1,2, Xinguan Dai1,2

  • 1College of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.

Sensors (Basel, Switzerland)
|January 21, 2023
PubMed
Summary

This study introduces a novel method for multi-sensor data fusion, improving accuracy by dynamically adjusting for evidence conflict and fuzziness. The new approach enhances recognition reliability in complex environments.

Keywords:
D-S evidence theorymaximum entropymulti-sensor data fusionthe weight combinationtrust discount

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Area of Science:

  • Sensor Fusion
  • Artificial Intelligence
  • Pattern Recognition

Background:

  • Single sensors struggle with accuracy in complex environments.
  • Existing multi-sensor fusion methods inadequately address evidence conflict and fuzziness.

Purpose of the Study:

  • To propose a new evidence weight combination and probability allocation method for multi-sensor data fusion.
  • To enhance recognition accuracy and reliability by considering evidence conflict and fuzziness.

Main Methods:

  • Calculated evidence fuzziness using the maximum entropy principle.
  • Incorporated evidence conflict into fusing results.
  • Combined fuzziness and conflict to calculate trusted discount and reallocate probability functions.
  • Utilized Dempster's combination rule for evidence fusion.

Main Results:

  • Demonstrated that existing methods yield counterintuitive results with high-conflict, high-clarity evidence.
  • Validated the proposed method's effectiveness through comparative experiments.
  • Achieved higher fusing accuracy and reliability in behavior recognition on the PAMAP2 dataset.
  • Improved fusing accuracy by approximately 3.3% over traditional methods and 1.7% over existing improved methods.

Conclusions:

  • The proposed method dynamically adjusts weights for fuzziness and conflict, overcoming limitations of existing techniques.
  • This approach leads to more accurate and reliable multi-sensor data fusion for behavior recognition.