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MEMS vehicle-mounted navigation and positioning method

For vehicle navigation, when the satellite signal is invalid, only rely on satellite navigation system will affect the normal driving of vehicle. Through the MEMS inertial navigation and the vehicle wheel speed sensor is an effective complementary measure of vehicle navigation. However, for inertial navigation systems, low cost MEMS inertial navigation sensors have large measurement errors, and these accumulated errors eventually lead to erroneous positioning results. In this paper, based on the integrated navigation system, we study an extended Kalman filtering algorithm for the absence of satellite navigation signals. By analyzing two different motion state of vehicles, including straight linear motion and turning motion. A new method was put forward, that is two kinds of different mixed Kalman filtering algorithm. By using this velocity information at the moment before, such as the speed of the GPS receiver, the speed of the wheel speed sensor and MEMS inertial navigation, this paper puts forward the velocity error as the observations. Through the actual experiment shows that the new method greatly improve positioning accuracy and get better results.