Research On Key Technologies Of Compass/Gyroscope Integrated Azimuth Determining System

The integrated navigation system uses multiple navigation methods, and complements each other’s advantages to improve the navigation accuracy and reliability. As the most important navigation mode, the integrated navigation system is widely used in civilian and military area at present. And the errors compensation of subsystems and faults detection are the key technologies to improve the precision and reliability of the integrated navigation system. This paper mainly focuses on these key technologies of compass/gyroscope integrated azimuth determining system. The main works are the accurate analysis and compensation of compass and gyroscope, faults classification and detection for the common faults of integrated system. The main innovation points of this paper are shown as follows:1) An improved method is proposed for the compensation of the compass heading errors. The common compensation method for compass is difficult to be realized in outdoor environment, and the change of magnetic field has a great influence on compensation precision, so this paper proposes an adaptive compensation algorithm for compass to solve these problems under the support of the MEMS gyroscope. The Kalman filter (KF for short) is constructed on the basis of the compass heading error model. As the state variables of KF, the parameters of the compass heading error model change with the magnetic field to improve the adaptability to the environment. The weight of current azimuth for the calculation of the heading error model parameters is increased by using five conventional observations and two flexible observations, so the compensation precision for current azimuth is higher than the other azimuths. The experiment results show that in the environment with strong and changing magnetic interference, the maximum heading error decreases from ±14.5° to ±0.2° after adaptive compensation.2) An improved method is proposed for the compensation of the MEMS gyroscope bias drift. The bias drift of MEMS gyroscope has a complicated relationship with many factors such as temperature and supply voltage. So the bias drift model is complicated, and the calibration of the model will significantly improve the cost. Even so, it is hard to keep the high compensation precision over the entire temperature range of operation. So this paper uses the simplified bias drift model and the most appropriate parameters, which are estimated and updated by a strong tracking Kalman filter, to compensate the bias drift accurately. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range of ±1° in the dynamic outdoor experiment. It demonstrates that this adaptive compensation algorithm for MEMS gyroscope bias drift performs significantly better than KF and MLR, it can significantly reduce the cost and the preparing time, and keep the high compensation precision in the changing environment.3) An improved fault detection method is proposed for the compass/gyroscope integrated azimuth determining system. The compass and gyroscope integrated azimuth determining system needs not only the high precision, but also the high reliability. But in practical use, the precision of compass and MEMS gyroscope are affected by the magnetic interference and environmental factors especially temperature, so the reliability may be decreased. This paper divides the common faults of integrated system into four types; then deals with the measurements to get the corresponding fault detection values which are used to locate the fault source, basing on the analysis of the characteristic of each kind of fault; and the warning stage is used to reduce the delay. The experiment results show that this algorithm can detect and distinguish 0.05°/s bias fault,0.006°/s rate ramp fault and two kinds of magnetic interference fault, it performs much better than state χ2 detection method and residual χ2 detection method on detection precision and timeliness. It demonstrates that this method improves the accuracy of detectable fault, and finds the fault source accurately, so the appropriate method can be used to decrease the influence of fault, and improve the utilization of available information and the system reliability.