Rotational inertial navigation system (RINS) could improve navigation performance by rotating the inertial measurement unit (IMU) with gimbals, and error parameters could be self-calibrated as well. However, accelerometers size effect would cause more influence on navigation accuracy in RINS than a strapdown inertial navigation system because of gimbals rotation, which should be calibrated and compensated for high-end application. In this paper, size effect and other IMU errors are calibrated through optimal estimation with navigation errors in triaxis RINS. The rotation scheme is designed by the characteristics of size effect and other errors, and all errors are verified to be observable by piece-wise constant system method and singular value decomposition method. The self-calibration method is tested by simulations and experiments. The calibration repeatability of size-effect parameters is less than 0.15 cm, while other IMU errors reach higher calibration accuracy compared with traditional methods. Navigation experiments indicate that the sharply changing velocity errors are greatly corrected after compensation, and position and velocity accuracy have improved 30% and 70%, respectively, during 12-h navigation experiment. Therefore, the navigation performance of RINS could be further improved in both the short term and long term with the proposed self-calibration method.

Alpha stable noise, determined by four parameters, has been found in the random error of a laser gyroscope. Accurate estimation of the four parameters is the key process for analyzing the properties of alpha stable noise. Three widely used estimation methods—quantile, empirical characteristic function (ECF) and logarithmic moment method—are analyzed in contrast with Monte Carlo simulation in this paper. The estimation accuracy and the application conditions of all methods, as well as the causes of poor estimation accuracy, are illustrated. Finally, the highest precision method, ECF, is applied to 27 groups of experimental data to estimate the parameters of alpha stable noise in a laser gyroscope’s random error. The cumulative probability density curve of the experimental data fitted by an alpha stable distribution is better than that by a Gaussian distribution, which verifies the existence of alpha stable noise in a laser gyroscope’s random error.

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.