This paper develops an improved system-level calibration method for the strapdown inertial navigation system (SINS). It solves the problems of too many dimensions of state variables and installation error matrix coupling. To solve the two problems, the improved system-level calibration method derives the physical causes of the installation error matrix. The installation error matrix is decomposed into skew symmetric matrix and symmetric matrix. Then the appropriate system-level calibration scheme is designed based on the velocity error equations and attitude error equations. The designed system-level calibration scheme separates the coupled installation error matrix parameters by changing the value of strapdown matrix. Simulation results demonstrate that the proposed method is feasible. The relative errors of gyro calibration parameters are between 0.03% and 0.70%, and the relative errors of accelerometer calibration parameters are between 0.01% and 0.24%. For comparison, the navigation and positioning experiments are also performed to evaluate the proposed method. The results indicate that the improved system-level calibration scheme is better than the traditional system-level calibration scheme. The positioning error is reduced by more than 30%.

As an important deterministic error of the inertial measurement unit (IMU), the installation error has a serious impact on the navigation accuracy of the strapdown inertial navigation system (SINS). The impact becomes more severe in a highly dynamic application environment. This paper proposes a new IMU calibration model based on polar decomposition. Using the new model, the installation error is decomposed into a nonorthogonal error and a misalignment error. The compensation of the IMU calibration model is decomposed into two steps. First, the nonorthogonal error is compensated, and then the misalignment error is compensated. Based on the proposed IMU calibration model, we used a three-axis turntable to calibrate three sets of strapdown inertial navigation systems (SINS). The experimental results show that the misalignment errors are larger than the nonorthogonal errors. Based on the experimental results, this paper proposes a new method to simplify the installation error. This simplified method defines the installation error matrix as an antisymmetric matrix composed of three misalignment errors. The navigation errors caused by the proposed simplified calibration model are compared with the navigation errors caused by the traditional simplified calibration model. The 48-h navigation experiment results show that the proposed simplified calibration model is superior to the traditional simplified calibration model in attitude accuracy, velocity accuracy, and position accuracy.

The Global Navigation Satellite System/Strapdown Inertial Navigation System (GNSS/SINS) integrated navigation system is an important technology for UAV measurement and vehicle movement measurement. But in the operational process of the GNSS/SINS integrated navigation system, the GNSS signal is vulnerable to external interference, resulting in abnormal system measurement data, and system faults. These faults will reduce the navigation and positioning performance of the system and reduce the measurement accuracy of the system. Aiming at this problem, a GNSS/SINS fault detection and robust adaptive algorithm based on sliding average smooth bounded layer width is proposed. The algorithm evaluates the system measurement data based on the innovation residual and incorporates the sliding average filter to design the fault detection function based on the smooth bounded width layer. Accurate detection of system faults using fault detection function. The fault detection function value is used to construct the robust cofactor matrix to correct the measurement error in real-time, to improve the accuracy and robustness of the state estimation. The experimental results show that: The proposed algorithm in the paper compares with two traditional robust adaptive algorithms based on smooth bounded layer fault detection and residual chi-square fault detection. The fault detection rates for small step faults show an increase of more than 44.26% and 9.54%, respectively. Similarly, for slowly varying faults, the fault detection rates exhibit an increase of more than 29.32% and 13.56%, respectively. Throughout the fault, the filtering accuracy demonstrates an increase of more than 16.52% and 15.47%, respectively. The algorithm effectively improves the measurement accuracy of the GNSS/SINS integrated navigation system.

In the strapdown inertial navigation system (SINS), which is based on a high-precision rotary table for system-level calibration, the complexity of the transposition scheme and the long calibration time will inevitably lead to low calibration accuracy of the error parameters. These calibration results will seriously affect the navigation accuracy of SINS. In addition, the small observability of the error parameters often leads to unreliable results of system-level calibration, thus affecting the navigation accuracy of SINS. Here, we propose an improved observability calculation method and design a system-level calibration scheme with 4-position continuous rotation. First, we establish a calibration error model containing 24 error parameters and a 30-dimensional Kalman filter model. Then, based on the observability analysis method, we innovatively demonstrate the feasibility of the 3-axis rotation scheme. Finally, an efficient system-level calibration scheme for 4-position continuous transposition is designed, which can calibrate all the state variables. At the same time, compared with the popular 19-position calibration method, the calibration scheme in this paper improves the positioning accuracy by 20.49% and the velocity accuracy by 13.77%.