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.
Generally, in order to ensure the reliability of Navigation system, vehicles are usually equipped with two or more sets of inertial navigation systems (INSs). Fusion of navigation measurement information from different sets of INSs can improve the accuracy of autonomous navigation effectively. However, due to the existence of misalignment angles, the coordinate axes of different systems are usually not in coincidence with each other absolutely, which would lead to serious problems when integrating the attitudes information. Therefore, it is necessary to precisely calibrate and compensate the misalignment angles between different systems. In this paper, a dynamic calibration method of misalignment angles between two systems was proposed. This method uses the speed and attitude information of two sets of INSs during the movement of the vehicle as measurements to dynamically calibrate the misalignment angles of two systems without additional information sources or other external measuring equipment, such as turntable. A mathematical model of misalignment angles between two INSs was established. The simulation experiment and the INSs vehicle experiments were conducted to verify the effectiveness of the method. The results show that the calibration accuracy of misalignment angles between the two sets of systems can reach to 1″ while using the proposed method.
In the rotational inertial navigation system (RINS), the inertial measurement unit (IMU) rotates around the motor shaft, which will stimulate the gyro scale factor asymmetry, so it needs to be effectively calibrated and compensated. In this paper, the influence of gyro scale factor asymmetry on angular velocity error, angle error and velocity error is analysed, and a self-calibration method for it is proposed in the RINS using velocity errors as measurements. A self-calibration rotation strategy is designed for the dual-axis RINS so as to carry out self-calibration for the scale factor asymmetry of x-gyro, y-gyro and z-gyro. The feasibility of this scheme is verified by theoretical analysis and simulation. Experiments are carried out on a set of dual-axis RINS based on fiber optic gyroscope, and the scale factor asymmetry calibration results of three gyros are 2.56 ppm, 10.01 ppm and 0.88 ppm, respectively. In the navigation experiment, the navigation error is improved by 57.45% after compensating the gyro scale factor asymmetry error, which fully illustrates the significance of the proposed self-calibration method in improving the navigation performance of RINS.
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