Underground Coal Mine Positioning System Based on RSSI Positioning Algorithm Improved through the BP Learning Training

The influence of the coal mine geographical environment on the electromagnetic transmission might result in the difficulty of wireless positioning under the mine. Concerning that the influence of the underground working face on the wireless signal attenuation is mainly reflected on the path attenuated electricity and based on the underground geographical differences, two corresponding electromagnetic loss model are established. Due to low energy consumption and no need for hardware devices, RISS algorithm is suitable to be used in the underground coal mine. However, the problem of large error and poor precision still exists. This paper first introduces the standard deviation threshold, TSA, decided by the practical environment; then compares it with the standard deviation, RSA, obtained by the calculation of every target node to finally obtain the modified value of RSS. Based on that, the BP algorithm is introduced for learning training, improvement of the positioning error rate and the system’s positioning precision.