Underground Coal Mine Positioning System Based on RSSI PositioningAlgorithm Improved Through the BP Learning Training

摘要

The influence of the coal mine geographic environment on the electromagnetic transmission might result in thedifficulty of wireless positioning under the mine. Concerning that the influence of the underground working face on thewireless signal attenuation is mainly reflected through the electricity path attenuated and based on the undergroundgeographic differences, two corresponding electromagnetic loss models are established. Under the conditions of lowenergy consumption and no need for hardware devices, RISS algorithm is found suitable to be used in the undergroundcoal mine. However, the problems of large error and poor precision still exist. This paper first introduces the standarddeviation threshold, TSA, as 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 BPalgorithm is introduced for learning training, improvement of the positioning error rate and the system’s positioningprecision.