GSM Mobile Station Location Using Reference Stations and Artificial Neural Networks

Authors:
Zoran Salcic
Affiliations:
The University of Auckland, Department of Electrical and Electronic Engineering, Private Bag 92019, Auckland, New Zealand E-mail: z.salcic@auckland.ac.nz
Venue:
Wireless Personal Communications: An International Journal
Year:
2001

Citing 3
Cited 1

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Abstract

In this paper we present a novel approach to the automatic GSM mobile station location. The approach is based on measurement of radio signal strengths from a number of the neighboring base stations (antennas) and estimation of the mobile station position using trained artificial neural network (ANN) models. First, we present an improved version of our previous positioning back propagation (BP) ANN multi-level perceptron (MLP) model that further improves positioning accuracy. Then, we extend the MLP primary ANN model by introducing correctional factors obtained from a number of reference stations with known positions. Two new models with the improved location accuracy, both aimed at real-time application, are presented. The first model is using differential range to improve the estimated location of the mobile station. The second is using small-scale secondary neural networks trained with data obtained from reference stations, in addition to the primary ANN, to correct location accuracy.