Haiyu Lan, Yashar Balazadegan Sarvrood, Adel Moussa and Naser El-Sheimy, Profound Positioning Inc., Canada

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Recently, navigation and localization for ground vehicles have become a hot research topic. The use of low-cost MEMS (micro-electromechanical system) inertial sensors and GNSS (global navigation satellite system) receivers are widespread in this industry. To perform ZUPT (zero velocity update) measurement update for the navigation filter to correct PVA (position, velocity, attitude) errors of a typical inertial navigation system (INS) using inertial sensors, in this paper, a method of detecting ZUPT phases of a typical ground vehicle is proposed based on machine learning. The algorithm detects stationary and dynamic movements of vehicles through SVM (support vector machine), which is well-known for solving binary classification problems. The proposed SVM-based ZUPT detection approach can improve the accuracy of a GNSS/INS integrated system by replacing the traditional threshold-based detector. The test and results indicate that the proposed method could recognize the ZUPT phases of a vehicle with high efficiency and accuracy.