A MEMS-Assisted GNSS Signal Uninterrupted Tracking Method Based on Adaptive Motion Constraints

In cities, global navigation satellite system (GNSS)-based land vehicle navigation is often interrupted due to the occlusion of satellite signals. Even after GNSS signals recover from occlusion, it will take some time for the GNSS receiver to acquire and track the signals again. To restore the navigation service as soon as possible, a micro-electromechanical system (MEMS)-assisted signal tracking method based on adaptive motion constraints is proposed. During GNSS outage, the vehicle’s motion state is identified by detecting the output of MEMS sensor. Meanwhile, in order to improve the identification accuracy of motion states, fuzzy inference is adopted to adaptively adjust the detection threshold, and then, motion constraints-derived virtual measurements are generated and used to maintain the work of the GNSS/inertial navigation system (INS) loosely integrated navigation system. Finally, the parameters of GNSS signals are predicted in real time based on the navigation solution, and the uninterrupted tracking of the occluded GNSS signals is realized. The proposed method is verified by utilizing the low-grade MEMS in different motion states. Experimental results show that the identification of motion states is more accurate based on fuzzy inference, and the prediction errors are reduced with adaptive motion constraints and kept within the tracking range of GNSS receiver. As a result, GNSS signals that are lost for up to 50 s can be locked immediately when they are recovered.