(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 3, No. 12, 2012 25 | Page www.ijacsa.thesai.org Pattern Recognition-Based Environment Identification for Robust Wireless Devices Positioning Nesreen I. Ziedan Computer and Systems Engineering Department Faculty of Engineering, Zagazig University Zagazig, Egypt 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. Keywords-component; GPS; GNSS; machine learning; pattern recognition; PCA; PNN; multipath. I. INTRODUCTION A Global Navigation Satellite System (GNSS) [1, 2] is a radio navigation system that employs spread spectrum techniques to transmit ranging signals and navigation data. The ranging signals are used by a GNSS receiver to identify the visible GNSS satellites and measure the distance between the visible GNSS satellites and the GNSS receiver. The measured distances are used with the navigation data to solve the navigation equation to determine the user’s 3-diemntional position and velocity. Examples of GNSS systems are the US Global Positioning System (GPS), the Russian GLONASS, and the European Galileo Navigation System. GNSS receivers perform three main functions: signal acquisition, signal tracking, and navigation message decoding. Signal acquisition identifies the visible satellites and provides rough estimates of the Doppler frequency, f d , and the ranging code delay, IJ. Signal tracking applies closed loop tracking techniques to provide continuous accurate estimates of the carrier phase, the Doppler frequency, the Doppler rate, and the code delay. Those estimates are used to measure the distance between the GNSS satellite and the receiver. GNSS receivers can give positioning accuracy up to a few millimeters when the receiver is stable and has a clear view of the sky, where the Line-of-Sight (LOS) signal is received with strong power. However, in environments like urban, suburban, and indoor, the received signals suffer from attenuation and multipath errors because of the surrounding objects [3]. In addition, the user’s mobility behavior can subject the receiver to changing and unstable signals dynamics. This leads to deterioration in the tracking and positioning accuracy. Multipath signals are a major error source. They appear when the GNSS satellite signals are reflected off different surfaces and propagated to the receiver's antenna via different paths. This leads to the reception of several versions of the same signal, which causes tracking errors. Analysis of the received signals indicated that their characteristics depend on the surrounding environment [4, 5, 6, 7, 8]. Urban, sub-urban and indoor environments generate different characteristics, which include multipath signals' parameters like the number and duration of echoes and signals power, and LOS signal's parameters like amplitude fluctuation, Doppler shift and rate. Different tracking strategies are needed for each environment to mitigate multipath errors and maximize the tracking performance. There have been numerous tracking strategies that are optimized for specific signal condition or environment. For example, conventional tracking techniques [1, 2] are used with strong signals. Kalman Filter based techniques are used with weak signals [3, 9]. Tightly-coupled GNSS with Inertial Navigation System (INS) techniques are used with weak interrupted signals or blocked signals [10]. Open-loop batch processing, and combined batch and sequential processing techniques are used in high dynamic applications [11]. Particle Filter-based techniques are used for tracking in multipath environments [12, 13, 14]. A GNSS receiver is usually tuned to one tracking technique, and there have been no methods that enable a receiver to change its tracking strategy based on the surrounding environment. This paper introduces a machine- learning pattern recognition algorithm to identify the surrounding environment, and hence enable the implementation