Dual-axis rotational inertial navigation system (DRINS) can achieve self-calibration of error parameters through a reasonable rotation scheme. However, the traditional self-calibration methods rely on the external reference information, which fail in global navigation satellite system (GNSS)-denied environments. In long-endurance marine navigation applications, carriers are usually equipped with multiple sets of DRINS. The geometric relationship between the DRINSs can serve as constraint observation for error parameter estimation. Therefore, this article proposes a collaborative calibration method with the navigation information fusion of dual DRINSs in GNSS-denied environments. Considering scale factor error, installation error, gyro drift, accelerometer bias, and accelerometer size-effect error introduced by the rotation of dual DRINSs, a 66-D Kalman filter is established based on the geometric constraint observation. Then, a novel collaborative calibration scheme is designed through analyzing the principles of error state observability for dual DRINSs. Simulations and experiments show that all error parameters can be precisely estimated by the proposed method with the designed calibration scheme and the calibration accuracy could satisfy the demand of long-endurance marine navigation.

Autonomous underwater vehicles (AUVs) play an increasingly essential role in the field of polar ocean exploration, and the Doppler velocity log (DVL)-aided strapdown inertial navigation system (SINS) is widely used for it. Due to the rapid convergence of the meridians, traditional inertial navigation mechanisms fail in the polar region. To tackle this problem, a transverse inertial navigation mechanism based on the earth ellipsoidal model is designed in this paper. Influenced by the harsh environment of the polar regions, unknown and time-varying outlier noise appears in the output of DVL, which makes the performance of the standard Kalman filter degrade. To address this issue, a robust Kalman filter algorithm based on Mahalanobis distance is used to adaptively estimate measurement noise covariance; thus, the Kalman filter gain can be modified to weight the measurement. A trial ship experiment and semi-physical simulation experiment were carried out to verify the effectiveness of the proposed algorithm. The results demonstrate that the proposed algorithm can effectively resist the influence of DVL outliers and improve positioning accuracy.

Rotational inertial navigation systems (RINSs) are often employed in long-endurance navigation due to their high-precision characteristics. A redundant configuration is often adopted in practice to cope with potential faults. To effectively monitor the dual single-axis RINSs redundant system, this paper proposes a novel real-time fault monitoring algorithm that integrates adaptive filtering with parameter estimation. The adaptive fault monitoring filter based on the state transformation error model with attitude observations increases the sensitivity of the filter to potential faults. Based on analysis of the filter estimation, a robust fault diagnosis criterion is proposed. Furthermore, the drift variation fitting mechanism is further proposed to address the challenge in monitoring faults of azimuth gyros. Experiments conclusively demonstrate the proficiency of the proposed method in achieving precise and swift fault monitoring.

Dual-axis rotational inertial navigation systems (DRINSs) have been widely used in marine navigation due to their high accuracy. However, the long-term operation of a DRINS over weeks poses a significant challenge to its reliability. In order to address the fault diagnosis challenges faced by DRINSs on long-endurance vessels in global navigation satellite system (GNSS)-denied environments, this paper proposes a fault diagnosis algorithm for redundant DRINSs based on geometric constraint observation. The mechanization of dual DRINSs is implemented using a globally referenced framework. A residual-normalized strong tracking filter based on geometric constraint observation is employed to estimate the fault states of the dual DRINSs. A highly robust fault diagnosis method is proposed to detect and diagnose faults in the inertial devices of dual DRINSs. The experimental results show that the proposed algorithm exhibits excellent performance with a diagnostic accuracy of 98.67% and low diagnostic delay.