Fingerprinting Localization Method Based on TOA and Particle Filtering for Mines

Author:

Song Boming12,Zhang Shen12,Long Jia12,Hu Qingsong12ORCID

Affiliation:

1. Internet of Things (Perception Mine) Research Center, China University of Mining & Technology, Xuzhou 221008, China

2. School of Information and Control Engineering, China University of Mining & Technology, Xuzhou 221008, China

Abstract

Accurate target localization technology plays a very important role in ensuring mine safety production and higher production efficiency. The localization accuracy of a mine localization system is influenced by many factors. The most significant factor is the non-line of sight (NLOS) propagation error of the localization signal between the access point (AP) and the target node (Tag). In order to improve positioning accuracy, the NLOS error must be suppressed by an optimization algorithm. However, the traditional optimization algorithms are complex and exhibit poor optimization performance. To solve this problem, this paper proposes a new method for mine time of arrival (TOA) localization based on the idea of comprehensive optimization. The proposed method utilizes particle filtering to reduce the TOA data error, and the positioning results are further optimized with fingerprinting based on the Manhattan distance. This proposed method combines the advantages of particle filtering and fingerprinting localization. It reduces algorithm complexity and has better error suppression performance. The experimental results demonstrate that, as compared to the symmetric double-sided two-way ranging (SDS-TWR) method or received signal strength indication (RSSI) based fingerprinting method, the proposed method has a significantly improved localization performance, and the environment adaptability is enhanced.

Funder

fundamental research funds for the central universities

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

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