GISEIA-EMM: A High-Accuracy GPS-Inertial State Estimator for In-Motion Alignment Based on Extended Magnitude Matching Method

The initial alignment is a critical stage for a strapdown inertial navigation system (SINS) and global positioning system (GPS) integrated navigation system. Currently, two major factors degrade the performance of SINS/GPS in-motion initial alignment, i.e., outliers in GPS measurements and cumulative low-accuracy inertial measurement unit (IMU) bias errors. This article considers both factors and proposes GISEIA-EMM: a high-accuracy GPS-inertial state estimator for in-motion alignment based on extended magnitude matching (EMM) method. First, we use the full integral method and non-interpolation procedure to construct the vector observation, which reduces the number of outliers and improves the accuracy of outlier detection. Second, we use an error-state extended Kalman filter (ESEKF), based on an augmented state-space model where the reference vector is regarded as a state, to suppress cumulative IMU bias errors, which improves the alignment accuracy. Third, we propose an EMM method, with the non-drifted expected normalized magnitude error, to detect and eliminate outliers in GPS measurements, which makes the alignment process stable. Simulation and field test results demonstrate that GISEIA-EMM can effectively address the negative impact of the two factors.