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Practical Performance Analysis for Multiple Information Fusion Based Scalable Localization System Using Wireless

Yubin Zhao1,2, Xiaofan Li3, Sha Zhang4

  • 1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China. zhaoyb@siat.ac.cn.

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|August 27, 2016
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Summary
This summary is machine-generated.

This study introduces a scalable Cramér-Rao lower bound (CRLB) method to analyze wireless localization system performance. The framework fuses multiple information sources adaptively, providing a practical limit for complex environments.

Keywords:
Bayesian estimationCramér–Rao lower boundindoor localizationnon-line-of-sightwireless sensor network

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Area of Science:

  • Engineering
  • Computer Science
  • Signal Processing

Background:

  • Practical wireless localization requires integrating auxiliary information, sensing devices, and environmental factors.
  • Technical challenges include state propagation, target-anchor geometry, and Non-line-of-sight (NLOS) identification.
  • An efficient framework is needed to exploit diverse information for system design.

Purpose of the Study:

  • To propose a scalable method for analyzing wireless localization system performance using the Cramér-Rao lower bound (CRLB).
  • To develop a framework that adaptively fuses multiple information sources for improved localization accuracy.
  • To provide a practical fundamental limit for localization systems in complex environments.

Main Methods:

  • An abstract function models wireless localization systems.
  • The Cramér-Rao lower bound (CRLB) is adapted by partitioning the unknown vector and Fisher information matrix.
  • The method fuses information from sources like hidden Markov models, map matching, and NLOS mitigation.

Main Results:

  • The proposed CRLB method provides a practical fundamental limit, adaptable to complex environments and multiple information fusions.
  • Analysis of wireless sensor network-based indoor localization demonstrates the method's effectiveness.
  • The influence of hybrid LOS/NLOS channels, building layout, and height differences on localization accuracy is analyzed.

Conclusions:

  • The developed method offers a more adaptable and practical approach to analyzing localization system performance compared to traditional CRLBs.
  • It effectively exploits all available information for indoor localization systems, serving as a crucial evaluation indicator.
  • The findings contribute to more efficient and effective practical localization system design.