Many in-motion coarse alignment methods have been proposed and have achieved good results for the strapdown inertial navigation system (SINS) aided by the global positioning system (GPS). However, most existing methods are only applicable to the high-accuracy SINS and the potential influence of the outliers in the GPS outputs are usually ignored. In this article, we consider two practical difficulties: first, the accuracy of the vector observations is degraded by the gyroscope bias; second, the GPS outputs are affected by outliers. To address these issues, we propose a new robust quaternion unscented Kalman filter (RQUKF) method for the low-accuracy SINS and GPS integrated system. First, the RQUKF method is used to estimate the gyroscope bias with the aid of the GPS. Second, to obtain the reliable GPS outputs, the magnitude matching method based on the Huber's robust theory is utilized to detect and eliminate the outliers. The simulation and field tests are conducted and the results illustrate that even if the GPS outputs are affected by outliers, the proposed method still has the higher accuracy in terms of the yaw angle, than traditional algorithms without outliers in the GPS outputs.

In this article, a novel robust initial alignment method for the strapdown inertial navigation system (SINS) and global positioning system (GPS) integrated system is proposed to eliminate the outliers containing in the outputs of GPS. There are two innovations in the proposed method. First, the traditional quaternion Kalman filter method is often used to estimate the constant attitude quaternion in the swaying base. However, this article devises the new quaternion state model, which can be used in the in-motion base, expanding the scope of application. Second, based on the Huber's robust theory and the in-motion quaternion Kalman filter state model, the novel algorithm called the robust quaternion Kalman filter method is devised. This method can construct the improved observation vectors to eliminate the outliers and estimate the constant attitude quaternion more accurately than the traditional in-motion method when GPS contains outliers. The experiment using the miniature inertial measurement unit (MIMU)-based SINS aided by GPS has shown the effectiveness of the proposed method.

The initial alignment is essential to guarantee the navigation performance of a strapdown inertial navigation system (SINS) and global positioning system (GPS) integration. Since a position reference vector is usually taken as an output, current filtering-based in-motion alignment methods that use position loci can only employ partial integration to reduce the accumulative inertial measurement unit (IMU) bias errors. However, the partial integration can amplify the negative impact of the GPS outliers, resulting in degraded alignment performance. In order to address this issue, we take the position reference vector as an augmented state to suppress the accumulative IMU bias errors, so that the full-integration-based position loci can be applied to reduce the negative impact of the GPS outliers. Specifically, we use an on-manifold error state extended Kalman filter (ESEKF) with the proposed full-integration-based state-space model to estimate position reference, velocity reference, attitude error, and IMU bias vectors. Moreover, since the widely used interpolation procedure can be avoided in our framework due to the existence of the full-integration-based state, we do not need to update the state at each IMU sampling instant, and thus the computational burden is greatly reduced. Experimental results show that our method achieves higher alignment precision than existing methods using position loci while maintaining a similar running time.

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.