Design and application research of mine underground disaster relief personnel positioning system based on MEMS sensor

Yue Zhao; Jianyong Wang; Chengyong Duan

doi:10.1117/12.2639253

15 July 2022 Design and application research of mine underground disaster relief personnel positioning system based on MEMS sensor

Yue Zhao, Jianyong Wang, Chengyong Duan

Author Affiliations +

Proceedings Volume 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022); 122582V (2022) https://doi.org/10.1117/12.2639253
Event: International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 2022, Qingdao, China

Abstract

In order to solve the problem of low positioning accuracy of disaster relief personnel and rescue efficiency when a disaster occurs in a coal mine, this paper proposes a disaster relief personnel positioning system for coal mine based on Micro-Electro-Mechanical System (MEMS) sensor. The proposed system uses the MPU9150 inertial sensor to obtain measurement data and the CC2530 microprocessor as the main control chip for data acquisition and processing. In the proposed system, the Pedestrian Dead Reckoning (PDR) algorithm is used to determine the step size based on the fusion expression between the walking frequency variance and acceleration, and the quaternion method is used to estimate pedestrian orientation angle. In order to reduce the error caused by the drift of the accelerometer and gyroscope, the extended Kalman filter is employed to correct the original data. The experimental results show that the positioning error of the proposed system is less than 1.6 m in 100 m. Thus, the proposed method can achieve high accuracy in the disaster relief personnel positioning in an underground coal mine.

Citation Download Citation

Yue Zhao, Jianyong Wang, and Chengyong Duan "Design and application research of mine underground disaster relief personnel positioning system based on MEMS sensor", Proc. SPIE 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 122582V (15 July 2022); https://doi.org/10.1117/12.2639253

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