A solution to the ill-conditioned GPS accuracy classification problem: Context based classifier

GPS localization has been attracting significant attention recently in many areas. Intelligent transportation systems, navigation systems, road tolling, and collision avoidance systems, are examples of applications that utilize the GPS technology for localization. However, localization accuracy remains a key issue that prevents such applications from delivering on their ultimate promise. The localization accuracy of any GPS system depends heavily on the methodology it uses to compute locations as well as the measurement conditions in its surrounding. The impact of the measurement conditions on the localization accuracy in itself is an intricate ill-conditioned problem due to the incongruent nature of the measurement process. This paper proposes a novel scheme to address localization accuracy. The scheme involves three steps, namely, classify instantaneous GPS accuracy based on measurement conditions, consolidate the consecutive classifications of the GPS Accuracy, and enhance the robustness of classifying the GPS accuracy for the following measurements. Real life comparative experiments are conducted to demonstrate the efficacy of the proposed scheme in classifying the GPS accuracy under various measurement conditions.