A data fusion architecture for enhanced position estimation in wireless networks

Abstract

The problem of position determination of a mobile user in a wireless network has been studied extensively-particularly in the context of military operations and emergency 911 service in cellular networks. The Global Positioning System (GPS) and network-based angle of arrival (AOA), time difference of arrival (TDOA), and time of arrival (TOA) techniques offer solutions to the position estimation problem. However, each method has limitations that prevent its adoption as a universal solution. Data fusion is a collection of methods and techniques for merging disparate types of information. We present a data fusion model for the enhanced accuracy of position estimates within wireless networks. We illustrate our model with several examples and conclude with recommendations for future research directions.