Article,
Pattern Recognition-Based Environment Identification for Robust Wireless Devices Positioning
International Journal of Advanced Computer Science and Applications(IJACSA), (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&\#39;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.
