A rotational inertial navigation system (RINS) could improve navigation performance by modulating the inertial sensor errors with rotatable gimbals. When an inertial measurement unit (IMU) rotates, the deviations between the accelerometer-sensitive points and the IMU center will lead to an inner lever-arm effect. In this paper, a self-calibration method of the inner lever-arm parameters for a tri-axis RINS is proposed. A novel rotation scheme with variable angular rate rotation is designed to motivate the velocity errors caused by the inner lever-arm effect. By extending all inner lever-arm parameters as filter states, a Kalman filter with velocity errors as measurement is established to achieve the calibration. The accuracy and feasibility of the proposed method are illustrated by both simulations and experiments. The final results indicate that the inner lever-arm effect is significantly restrained after compensation by the calibration results.

Getting a land vehicle’s accurate position, azimuth and attitude rapidly is significant for vehicle based weapons’ combat effectiveness. In this paper, a new approach to acquire vehicle’s accurate position and orientation is proposed. It uses biaxial optical detection platform (BODP) to aim at and lock in no less than three pre-set cooperative targets, whose accurate positions are measured beforehand. Then, it calculates the vehicle’s accurate position, azimuth and attitudes by the rough position and orientation provided by vehicle based navigation systems and no less than three couples of azimuth and pitch angles measured by BODP. The proposed approach does not depend on Global Navigation Satellite System (GNSS), thus it is autonomous and difficult to interfere. Meanwhile, it only needs a rough position and orientation as algorithm’s iterative initial value, consequently, it does not have high performance requirement for Inertial Navigation System (INS), odometer and other vehicle based navigation systems, even in high precise applications. This paper described the system’s working procedure, presented theoretical deviation of the algorithm, and then verified its effectiveness through simulation and vehicle experiments. The simulation and experimental results indicate that the proposed approach can achieve positioning and orientation accuracy of 0.2 m and 20″ respectively in less than 3 min.