Positioning and Calibration Method of Underground Personnel Based on Priori Features

YUAN Yazhou; SUN Xiaoqin; LI Yuefeng; GUAN Xinping

doi:10.11999/JEIT170749

Volume 40 Issue 6

May 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(6): 1323-1329

YUAN Yazhou, SUN Xiaoqin, LI Yuefeng, GUAN Xinping. Positioning and Calibration Method of Underground Personnel Based on Priori Features[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1323-1329. doi: 10.11999/JEIT170749

Citation:

YUAN Yazhou, SUN Xiaoqin, LI Yuefeng, GUAN Xinping. Positioning and Calibration Method of Underground Personnel Based on Priori Features[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1323-1329. doi: 10.11999/JEIT170749

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Positioning and Calibration Method of Underground Personnel Based on Priori Features

doi: 10.11999/JEIT170749

1.
2.
(School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

Funds:

The Natural Science Foundation of Hebei Province (F2017203084), The Postdoctoral Priority Funding of Hebei Province (B2017003009)

Received Date: 2017-07-25
Rev Recd Date: 2018-03-02
Publish Date: 2018-06-19

Abstract

Abstract

Focusing on the problem that the personnel positioning methods are seriously influenced by the indoor environment, big cumulative error and other issues, a method is proposed to correct the position, which combines the prior knowledge of the map and the heading recognition. Firstly, the dimension of the feature set is reduced by Linear Discriminant Analysis (LDA). Then, the heading of the underground personnel is classified and the special points are marked through combining Random Forest (RF) and the method of setting a threshold value. Finally, the movement trajectory of the underground personnel, which is obtained by the Pedestrian Dead Reckoning (PDR) algorithm, is corrected and updated by matching the special point with the prior knowledge of the roadway structure. The experimental results show that the pre-processing method of LDA can effectively improve the precision of the classifier by more than 6%. The proposed method can effectively reduce the cumulative error, with high accuracy and robustness. The activity recognition accuracy can reach 98%, which can achieve reliable real- time location.
- Indoor localization,
- Multi-sensors,
- Linear Discriminant Analysis (LDA),
- Random Forest (RF),
- Priori features

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References(22)

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