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Sensor reliability evaluation scheme for target classification using belief function theory.

Jing Zhu1, Yupin Luo, Jianjun Zhou

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This study introduces a novel sensor reliability evaluation method for target classification using belief function theory. The approach enhances dissimilarity measures and adaptively combines static and dynamic discounting for improved accuracy.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Sensor Fusion

Background:

  • Sensor reliability evaluation is crucial for accurate target classification within belief function theory.
  • Existing methods face challenges in measuring evidence dissimilarity and adaptively combining discounting factors.
  • Addressing these issues is key to improving the performance of sensor fusion systems.

Purpose of the Study:

  • To propose a novel solution for sensor reliability evaluation in target classification.
  • To design an improved dissimilarity measure and an adaptive discounting combination method.
  • To enhance the self-learning and self-adapting capabilities of sensor fusion systems.

Main Methods:

  • Developed an improved dissimilarity measure based on a dualistic exponential function.
  • Assessed static reliability using sensor local decisions and evidence dissimilarity.
  • Obtained dynamic reliability factors through dissimilarity between sensor output and consensus.
  • Introduced an adaptive combination method for static and dynamic discounting using Parzen-window and fuzzy theory.

Main Results:

  • The proposed dualistic exponential function-based dissimilarity measure effectively quantifies evidence differences.
  • The adaptive discounting combination method allows the fusion system to self-learn and adapt to changing sensor performance.
  • Experimental results on real databases show superior performance in target classification compared to existing methods.

Conclusions:

  • The proposed sensor reliability evaluation scheme significantly improves target classification accuracy.
  • The adaptive fusion system demonstrates robust self-learning and self-adapting capabilities.
  • This approach offers a promising solution for enhancing sensor fusion systems in dynamic environments.