Geolocation in mines with an impulse response fingerprinting technique and neural networks

Abstract

The location of people, mobile terminals and equipment is highly desirable for operational and safety enhancements in the mining industry. In an indoor environment such as a mine, the multipath caused by reflections, diffraction and diffusion on the rough sidewall surfaces, and the non-line of sight (NLOS) due to the blockage of the shortest path between transmitter and receiver are the main sources of range measurement errors. Due to the harsh mining environment, unreliable measurements of location metrics such as RSS, AOA and TOA/TDOA result in the deterioration of the positioning performance. Hence, alternatives to the traditional parametric geolocation techniques have to be considered. In this paper, we present a novel method for mobile station location using wideband channel measurement results applied to an artificial neural network (ANN). The proposed system, the Wide Band Neural Network-Locate (WBNN-Locate), learns off-line the location 'signatures' from the extracted location-dependent features of the measured channel impulse responses data for LOS and NLOS situations. It then matches on-line the observation received from a mobile station against the learned set of 'signatures' to accurately locate its position. The location accuracy of the proposed system, applied in an underground mine, has been found to be 2 meters for 90% and 80% of trained and untrained data, respectively. Moreover, the proposed system may also be applicable to any other indoor situation and particularly in confined environments with characteristics similar to those of a mine (e.g. rough sidewalls surface).