Abstract: Aiming at the problem that the use of radio frequency signals for underground personnel positioning is greatly affected by the environment, this paper proposes an LSTM geomagnetic positioning algorithm based on the Attention mechanism. The mine smart phone is used to acquire underground geomagnetic data in the way of continuous acquisition of paths. By analyzing the characteristics of spatial difference and time stability of the geomagnetic signal changes in underground roadways, a geomagnetic fingerprint database oriented to the underground topology is established. The Attention mechanism is introduced into the LSTM model to enable which can be trained and given different weights according to the correlation between the geomagnetic sequence and the position; the geomagnetic data collected in real time downhole is input into the model for position estimation. The experimental results show that learning the mapping relationship between the geomagnetic sequence and the corresponding position through the neural network LSTM can effectively improve the positioning accuracy.

Key words: geomagnetic positioning, attention mechanism, LSTM algorithm, underground coal mine