Information Fusion and Target Tracking: Information-Theoretic Sensor Selection

Nianxia Cao, Pramod K. Varshney, Engin Masazade, Sora Haley

Research output: Chapter in Book/Entry/PoemChapter

Abstract

This chapter studies the problem of sensor selection for target tracking in a wireless sensor network from an information-theoretic perspective. To balance the computation cost and tracking performance, a sensor selection metric based on mutual information (MI) upper bound is proposed. The performance of the proposed metric is compared with a Fisher Information and a MI-based sensor selection metric. Fisher information-based sensor selection is simple but achieves lower computational complexity, while MI-based sensor selection provides better tracking accuracy but is computationally expensive. Furthermore, a multiobjective optimization framework is considered for information fusion to reveal trade-offs between the number of active sensors and tracking performance.

Original languageEnglish (US)
Title of host publicationInformation-Theoretic Radar Signal Processing
PublisherWiley
Pages217-249
Number of pages33
ISBN (Electronic)9781394216956
ISBN (Print)9781394216925
DOIs
StatePublished - Jan 1 2024

Keywords

  • Fisher information
  • information fusion
  • information theory
  • multiobjective optimization
  • mutual information
  • sensor selection
  • target tracking

ASJC Scopus subject areas

  • General Engineering
  • General Computer Science

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