Abstract

This paper covers location determination in wireless cellular networks based on time difference of arrival (TDoA) measurements in a factor graphs framework. The resulting nonlinear estimation problem of the localization process for the mobile station cannot be solved analytically. The well-known iterative Gauss-Newton method as standard solution fails to converge for certain geometric constellations and bad initial values, and thus, it is not suitable for a general solution in cellular networks. Therefore, we propose a TDoA positioning algorithm based on factor graphs. Simulation results in terms of root-mean-square errors and cumulative density functions show that this approach achieves very accurate positioning estimates by moderate computational complexity.

Highlights

  • Positioning in wireless networks became very important in recent years

  • In [14, 15], Chen et al proposed a method for solving the positioning problem in an factor graphs (FGs) environment using time of arrival (ToA) measurements

  • We propose to use a more robust time difference of arrival positioning algorithm based on factor graphs

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Summary

INTRODUCTION

Positioning in wireless networks became very important in recent years. Services and applications based on accurate knowledge of the location of the mobile station (MS) will play a fundamental role in future wireless systems [1,2,3]. In this paper we concentrate on determination of the MS location by exploiting the already available communications signals This localization process is based on measurements in terms of time of arrival (ToA), time difference of arrival (TDoA), angle of arrival (AoA), and/or received signal strength (RSS) [2], provided by the base stations (BSs) or the MS, where the achievable accuracy is the highest with the timing measurements TDoA or ToA. To obtain a general solution, we introduce a TDoA positioning method using a factor graphs (FGs) framework in this paper. It provides estimates which achieve high accuracy with low complexity and it is suitable for distributed processing.

SYSTEM MODEL
POSITIONING BASED ON FACTOR GRAPHS
Factor graphs and the sum-product algorithm
Geometric fundamentals
TDoA positioning algorithm based on factor graphs
Implementation example
SIMULATION RESULTS
CONCLUSIONS
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