Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (5): 112-118.doi: 10.6040/j.issn.1672-3961.0.2018.356

• Machine Learning & Data Mining • Previous Articles     Next Articles

Lightweight self-adaptive CSI-based positioning algorithm in underground mine

Junmei YUE1(),Dongmei ZHANG2   

  1. 1. Department of Information Engineering and Automation, Shanxi Institute of Technology, Yangquan 045000, Shanxi, China
    2. College of Information and Computer Science, Taiyuan University of Technology, Jinzhong 030600, Shanxi, China
  • Received:2018-08-24 Online:2019-10-20 Published:2019-10-18
  • Supported by:
    国家自然科学基金项目(61401300);山西省应用基础研究项目(201601D021074);山西工程技术学院校级课题(201706003)

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Abstract:

To solve the problem of high cost and working hazard factor of traditional downhole positioning methods, a lightweight self-adaptive CSI-based positioning algorithm in underground mine was proposed. The fine-grained CSI was used to obtain higher positioning accuracy rather than coarse-grained RSSI, inverse fast Fourier transform was adopted to transform CSI data to channel impulse response so as to get the line-of-sight signal, an attenuation model of line-of-sight signal of CSI was built to implement accurate ranging, position features of existing point access points (APs) in wireless fidelity and characteristics of rock roadways was utilized to calculate orientation of target relative to AP, which finally completed location according to orientation and distance. LSA was adaptive to arbitrary deployment modes, and the corner recognition optimization algorithm was used to improve positioning accuracy. The experimental results showed that LSA method median error could reach 0.53 m and eliminate the need to deploy any positioning system in the well alone, the performance was superrior to CDPF and FILA.

Key words: channel state information, signal attenuation model, underground positioning

CLC Number: 

  • TP391

Fig.1

Frame of positioning algorithm"

Fig.2

Raw CSI data"

Fig.3

Signal strength changes with different delays"

Fig.4

The secondary rate of power with time on different paths"

Fig.5

Roadway environment and APs deployment scenarios"

Fig.6

Rangeing results"

Fig.7

The direction recognition accuracies with different sampling times"

Fig.8

The corner recognition accuracies with different sampling times"

Fig.9

Cumulative distribution of localization errors on different method"

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