State Transformation Extended Kalman Filter for SINS based Integrated Navigation System

This paper first gives further explanations of the State Transformation Extended Kalman Filter (ST-EKF) from the perspective of common frame velocity error definition. Then develops the loosely-coupled integrated navigation models for Strapdown Inertial Navigation System (SINS)/Global Navigation Positioning System (GNSS) integration, which includes the system error models and measurement models. In the framework of EKF, the propagation of the state and covariance should be executed as fast as possible in order to capture the dynamic change of specific force. For example, the propagation rate is the same as the SINS calculation rate. However, in the framework of ST-EKF, the propagation and measurement updating processes can be implemented simultaneously, which reduces the computation cost greatly. Land vehicle experiment by using a Micro-Electro-Mechanical-Systems (MEMS)-Inertial Measurement Unit (IMU) was conducted to validate the performance of the ST-EKF. Results showed that ST-EKF integrated navigation system had higher positioning when GPS signals were loss from multiple locations. Meanwhile, ST-EKF had higher yaw and velocity maintaining accuracy than EKF when under quasi-static parking situations.