Research On Compensation Technology Of High Precision Attitude Sensor

As an inherent requirement of modern warfare,the combination of attitude sensors and traditional suppressive weapons to improve the precision combat capability of weapons has become one of the important development directions of today’s weapon systems.In this context,how to improve the measurement accuracy and credibility of attitude sensors has become a research hotspot and key point in various countries.In order to reduce the pressure of Device Manufacturing in China and improve the measurement accuracy of the sensor,the self-developed XGZT-III high-precision attitude sensor is taken as the research object,and the high-precision attitude sensor compensation technology is studied according to its composition and working principle.1)In order to compensate for the systematic error of the attitude sensor,a twenty-four position calibration method and a forward and reverse rotation calibration method were studied.This calibration method uses the spatial symmetry position scheme to decouple the influence of the initial leveling error and random error of the experimental equipment on the accuracy of the systematic error compensation,and realizes the effective compensation of the systematic error of the attitude sensor.2)In order to compensate for the random error of the attitude sensor,the Allan variance method is first used to study the random error parameter identification technology of the attitude sensor.Aiming at the problem of insufficient Allan variance fitting accuracy caused by the uneven variance of Allan variance values in LS,a random error fitting based on IRLS is studied,which improves the fitting accuracy of random error parameters and provides an accurate basis for the determination of filtering effect;for the problem that traditional DAVAR cannot take into account both mutation tracking ability and variance confidence,an F-based adaptive window length DAVAR method is studied to achieve accurate evaluation of the dynamic characteristics of random error of attitude sensors.Secondly,a Sage-Husa adaptive filtering algorithm with noise estimation is studied on the random error compensation technique based on ARMA,and a Sage-Husa adaptive filtering algorithm with noise estimation is studied for the problem that the attitude sensor cannot fully meet the assumptions of Kalman filter noise characteristics caused by the actual application of the attitude sensor.Simulation results show that the "improved Sage-Husa filtering method" based on time series modeling can better improve the output accuracy of attitude sensors.3)Aiming at the problem of estimating accuracy when a single device cannot guarantee the long-term operation of the attitude sensor,the gyroscope and accelerometer fusion algorithm in the attitude sensor is studied.According to the high and low frequency error characteristics of the two sensors,Mahony filter is used for attitude fusion solution.Aiming at the problem of insufficient attitude accuracy caused by Mahony filtering after ignoring gyroscope noise,an EKF fusion algorithm with noise estimation is studied.Simulation results show that the proposed algorithm has smaller attitude solving error and higher credibility than Mahony filtering algorithm.Based on the above research,a high-precision attitude sensor accuracy detection system is designed and implemented in this paper,and the accuracy detection of the attitude sensor is used by the platform.Experimental results show that the compensation algorithm studied in this paper can effectively improve the measurement accuracy and credibility of high-precision MEMS attitude sensors,so that the accuracy of attitude sensors meets the factory requirements.