Indoor Geolocation with Received Signal Strength Fingerprinting Technique and Neural Networks

Abstract

The location of people, mobile terminals and equipments is highly desirable for operational enhancements and safety reasons in indoor environments. In an in-building environment, the multipath caused by reflection and diffraction, and the obstruction and/or the blockage of the shortest path between transmitter and receiver are the main sources of range measurement errors. Due to the harsh indoor 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 method for mobile station location using narrowband channel measurement results applied to an artificial neural network (ANN). The proposed system learns off-line the location ‘signatures’ from the extracted location-dependent features of the measured 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 precision of the proposed system, applied in an in-building environment, has been found to be 0.5 meter for 90% of trained data and about 5 meters for 45% of untrained data.