Fingerprinting Localization Method Based on TOA and Particle Filtering for Mines
Joint Authors
Zhang, Shen
Song, Boming
Long, Jia
Hu, Qingsong
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-09
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Song, Boming& Zhang, Shen& Long, Jia& Hu, Qingsong. 2017. Fingerprinting Localization Method Based on TOA and Particle Filtering for Mines. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1190107
Modern Language Association (MLA)
Song, Boming…[et al.]. Fingerprinting Localization Method Based on TOA and Particle Filtering for Mines. Mathematical Problems in Engineering No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1190107
American Medical Association (AMA)
Song, Boming& Zhang, Shen& Long, Jia& Hu, Qingsong. Fingerprinting Localization Method Based on TOA and Particle Filtering for Mines. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1190107
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1190107