Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Digital Object Identifier (DOI) : 10.14569/IJACSA.2012.031204
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 3 Issue 12, 2012.
Abstract: There has been a continuous increase in the demands for Global Navigation Satellite System (GNSS) receivers in a wide range of applications. More and more wireless and mobile devices are equipped with built-in GNSS receivers; their users’ mobility behavior can result in challenging signal conditions that have detrimental effects on the receivers’ tracking and positioning accuracy. A major error source is the multipath signals, which are signals that are reflected off different surfaces and propagated to the receiver's antenna via different paths. Analysis of the received multipath signals indicated that their characteristics depend on the surrounding environment. This paper introduces a machine-learning pattern recognition algorithm that utilizes the aforementioned dependency to classify the multipath signals’ characteristics and identify the surrounding environment. The identified environment is utilized in a novel adaptive tracking technique that enables a GNSS receiver to change its tracking strategy to best suit the current signal condition. This will lead to a robust positioning under challenging signal conditions. The algorithm is verified using real and simulated Global Positioning System (GPS) signals with accurate multipath models.
Nesreen I. Ziedan, “Pattern Recognition-Based Environment Identification for Robust Wireless Devices Positioning” International Journal of Advanced Computer Science and Applications(IJACSA), 3(12), 2012. http://dx.doi.org/10.14569/IJACSA.2012.031204