The navigation accuracy of the rotational inertial navigation system (RINS) could be greatly improved by periodically rotating the inertial measurement unit (IMU) with gimbals. However, error parameters in RINS should be effectively calibrated and compensated. In this paper, a self-calibration method is proposed for tri-axis RINS using attitude errors and velocity errors as measurements. The proposed calibration scheme is designed as three separate steps, and a certain gimbal rotates continuously in each step. All the error parameters in the RINS are calibrated when the whole scheme finishes. The separate calibration steps reduce the correlations between error parameters, and the observability of errors in this method is clear to demonstrate according to the relations between navigation errors and error parameters when gimbals rotate. Each calibration step only lasts 12 min, thus gyro drifts and accelerometers biases could be regarded as constant. The proposed calibration scheme is tested in both simulation and actual tri-axis RINS, and simulation and experimental results show that all 23 error parameters could be well estimated in tri-axis RINS. A long-term vehicle navigation experiment results show that after calibration and compensation, the navigation performance has doubled approximately, and the velocity accuracy is less than 2 m s⁻¹ while the position accuracy is less than 1500 m, fully illustrating the significance of the proposed self-calibration method in improving the navigation performance of RINS.

Obtaining the land vehicle’s position, azimuth and attitudes autonomously and accurately is significant for vehicle based weapons’ combat effectiveness. High performance INS can provide high precision attitudes and azimuth reference information, however, it is usually very expensive. In addition, due to gyroscope drifts and accelerometer biases, INS navigation errors would diverge with time. To solve the problem, this paper proposed an integrated positioning and orientation method based on the FOG single-axis rotational INS (FRINS) and the odometer (OD). The proposed method adopted the rotation modulation technique to suppress the inertial sensor errors, particularly z-axis FOG drift, by which the navigation accuracy, especially the azimuth accuracy, can be improved. At the same time, the proposed method integrated the FRINS with odometer to suppress INS errors’ divergence with time. Combining the above rotation modulation technique and the integration of FRINS and OD, the proposed method could improve the integrated system positioning and orientation accuracy as well as reduce the integrated system cost. This paper presented the FRINS/OD integrated navigation system configuration, established the FRINS/OD integrated system errors model, and then verified the performance of proposed method by simulations. The results show that the proposed method could achieve positioning accuracy of 0.03% (of total traveled distance) and orientation of ±20″ respectively using RINS with FOGs of 0.1°/h and accelerometers of 500ug.

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.

Rotating modulation technique is a mature method which has been widely used in the rotational inertial navigation system (RINS). Inertial Measurement Unit (IMU) can be driven to rotate to modulate the inertial devices’ errors, in this way the position accuracy of RINS can be greatly improved. However, the outputs of attitude are easily affected for the existence of rotation mechanism. The rotation mechanism of tri-axis RINS consists of three pairs of motors and photoelectric encoders. The errors of the rotation mechanism mainly contain the installation errors of the gimbals, the installation errors of photoelectric encoder and the zero-position errors of the system; they have different effects on the outputs of the attitude. This paper researches the principle of attitude output accuracy loss in tri-axis RINS, the effects of different errors on attitude are analyzed and a self-calibration method based on recursive least squares (RLS) is proposed to calibrate these errors. Simulation and experiment results prove that the analyses is right and experiment results show that the proposed attitude compensation method can improve short-term attitude accuracy greatly, after the compensation, the attitude accuracy of the rotation direction is less than 12” , and the attitude accuracy of the other direction is less than 8” .

Considering all integration methods currently available for global positioning system (GPS) and inertial navigation system (INS) integrated navigation system, ultra‐tightly coupled (UTC) GPS/INS system is the best choice for accurate and robust navigation. However the performance of UTC GPS/INS system based on scalar tracking (ST) degrades in severe jamming environments. To solve this problem an innovative federated UTC GPS/INS system based on vector tracking (VT) is proposed. On the basis of UTC integration, three Kalman filters are developed to accomplish the goals of signal amplitude estimation, VT and integration navigation. By replacing the traditional ST loops with VT filter, the tracking ability is remarkably reinforced. More importantly, a new mathematical model of VT is proposed and the key innovation of this model is that it can provide adaptation ability to integration Kalman filter, so that the navigation performance can be improved under various jamming conditions. A simulation platform covering all procedures of GPS/INS integrated navigation is employed to verify the effectiveness of the proposed architecture. Tracking and navigation results show that the federated UTC GPS/INS integrated system based on VT improves both the tracking and navigation performance significantly under severe jamming environments compared with that of ST system.