This article investigates an encoder-based relative attitude observation method for self-calibration in a dual-axis rotational inertial navigation system (RINS). First, the calibration error of the gyroscope parameters caused by the velocity and position observations in systematic calibration is analyzed. Second, a relative attitude observation method utilizing photoelectric encoders for gyroscope parameter estimation is proposed. We apply the proposed method in our designed dual-axis RINS to verify its effectiveness in this article. Experimental results show that the calibration accuracy of the gyroscope can be improved to 0.0005 $^ \circ \text{/h}$ in bias, and 0.98 ppm in scale factor using the proposed method. The proposed self-calibration method can be utilized as a high-accuracy self-calibration method in the dual-axis RINS when the vehicle is in static condition.

This article investigates a novel Bluetooth odometer (BOD)-aided smartphone-based vehicular navigation system for satellite-denied environments. First, a wheel-mounted BOD is designed to measure rotational speed and send data to smartphones by Bluetooth transmission. Second, a nonlinear smartphone-based microelectromechanical system (MEMS) inertial measurement unit (IMU)/BOD error model is derived to solve the problem of azimuth divergence. Then, a position delay time model is proposed to compensate for the delay time of Bluetooth transmission. An Android app is developed to receive the BOD data from Bluetooth transmission, and the proposed BOD smartphone-based vehicular navigation algorithm provides positioning information in real time. In field tests, positioning accuracy reaches 0.38%D by using the proposed MEMS-IMU/BOD model. The designed scheme can be used as a low-cost and convenient solution for smartphone-based civilian vehicular autonomous navigation systems without changing the habits of drivers.

Dual-axis rotational inertial navigation systems (RINSs) can be categorized into inner-azimuth RINSs and outer-azimuth RINSs based on different frame structures. Previous systematic calibration methods mainly involved tri-axis turntables or inner-azimuth dual-axis turntables. However, the conventional calibration methods cannot be applied to outer-azimuth dual-axis RINSs due to the constraints on mechanical structure. Upon analyzing the inertial measurement unit (IMU) error stimulation of outer-azimuth dual-axis RINS under static conditions, specific IMU errors are revealed to be unobservable or weakly observable, which provides insights into the unavailability of self-calibration. To address this issue, an in-flight calibration method based on incremental azimuth observation is proposed. Specifically, we first provide a theoretical analysis in terms of error stimulation to lay a solid foundation for calibration with outer-azimuth dual-axis RINSs. Then, a novel approach based on incremental structure and azimuth information is proposed to tackle the problem of weak observability associated with gyro errors. Moreover, a principle for designing the calibration rotation scheme of IMU is developed and the corresponding 24-position rotation path is further created. Simulations and flight tests demonstrate that the estimations of all error parameters can achieve convergence with the proposed 40-dimensional (40-D) Kalman filter. By contrasting the maximum positioning errors of 1-hour pure inertial navigation tests, it is indicated that the proposed 40-D Kalman filter is found to be equally effective as the conventional 39-D Kalman filter, which fully illustrates the validity and feasibility of the proposed calibration method in outer-azimuth dual-axis RINSs.

In a long-endurance underwater vehicle rotational inertial navigation system (RINS), the rotation strategy significantly impacts navigation accuracy. To address the limitations of the dual-axis 16-position rotation modulation scheme, specifically, its large rotation angles and inability to modulate errors arising from the coupling of scale factors and Earth’s rotation rate. This article proposes a geocentric frame-based tri-axial 32-position rotation modulation method. This method suppresses navigation errors by altering the direction of the rotation axis and increasing the rotation sequence. It simultaneously isolates the Earth’s rotation rate, thereby eliminating the coupling error between inertial sensor scale factors and Earth rotation, and enhancing system navigation accuracy. Experimental results demonstrate that the system achieves a navigation positioning accuracy of approximately 2.2 nautical miles (n mile) over 72h. Compared to a single-axis RINS, positioning accuracy is improved by a factor of 8. Compared to a dual-axis RINS, positioning accuracy is enhanced by 0.75 n mile. These results validate the effectiveness of the proposed method in improving navigation accuracy.

Pedestrian Navigation System (PNS) is one of the research focuses of indoor positioning in GNSS-denied environments based on the MEMS Inertial Measurement Unit (MIMU). However, in the foot-mounted pedestrian navigation system with MIMU or mobile phone as the main carrier, it is difficult to make the sampling time of gyros and accelerometers completely synchronous. The gyro-accelerometer asynchronous time affects the positioning of PNS. To solve this problem, a new error model of gyro-accelerometer asynchronous time is built. The effect of gyro-accelerometer asynchronous time on pedestrian navigation is analyzed. A filtering model is designed to calibrate the gyro-accelerometer asynchronous time, and a zero-velocity detection method based on the rate of attitude change is proposed. The indoor experiment shows that the gyro-accelerometer asynchronous time is estimated effectively, and the positioning accuracy of PNS is improved by the proposed method after compensating for the errors caused by gyro-accelerometer asynchronous time.

Strapdown inertial navigation system/celestial navigation system (SINS/CNS) integrated navigation with narrow-field-of-view (NFOV) star tracker is widely employed in the aviation and aerospace fields contributed by its high accuracy and autonomy. However, due to the computational cost and data transmission of CNS, the output data of CNS and SINS cannot, be completely synchronized, which seriously affects the performance of integrated navigation, particularly in highly dynamic environments. To solve the problem above, this paper constructs a model and proposes an online calibration method for the asynchronous time between the outputs from SINS and CNS with NFOV star tracker. Firstly, the error characteristic of SINS/CNS asynchronous time is analyzed, and the imaging detection is revealed to be significantly aifected by the SINS/CNS asynchronous time in dynamic environments. Additionally, on the basis of the error characteristic analysis, a corresponding error model is established. Finally, an online calibration scheme of SINS/CNS asynchronous time is designed based on the backtracking navigation algorithm. Simulations and field experiments show that the online calibration method works well in calibrating the SINS/CNS asynchronous time and compensating for the tri-axis attitude misalignment angles of the SINS, affirming the efficacy and feasibility of the established model and proposed calibration method in this paper.