Research On Redundant MEMS-IMU Error Compensation And Parameter Identification Technology

The Strapdown Inertial Navigation System with redundant Micro-Electro-Mechanical System-Inertial Measurement Unit(MEMS-IMU)as the core component has the advantages of small size,high reliability,autonomy and strong anti-interference ability.It is widely used in the fields of guidance,robotics,etc.It also has the potential to be applied to aircraft,ships and other carriers.Since the MEMS device error is the main error source of the inertial navigation system,the MEMS-IMU error compensation technology is widely used to improve the navigation accuracy of the Strapdown Inertial Navigation System.However,on the one hand,the environmental adaptability and compensation accuracy of random error compensation need to be further studied.On the other hand,with the increase of the redundancy number and the complexity of the redundant structure,the difficulty of device error parameter identification is further increased.Based on the above problems,this paper will focus on the research of redundant MEMS-IMU error characteristics analysis,random error compensation and redundancy calibration technology.Firstly,the Allan variance method is used to analyze the random error characteristics of MEMS devices.Then the difference of MEMS-IMU error modeling between the three-axis orthogonal configuration structure and the redundant configuration structure is analyzed.They lay a foundation for the subsequent redundant MEMS-IMU random error compensation and parameter identification.Secondly,aiming at the problem that the traditional time series method can not directly realize the random error modeling in dynamic environment,an improved time series modeling method introducing sequence mean is proposed.In the aspect of random error compensation,the Sage-Husa adaptive filtering method with noise estimation is proposed because the random error of MEMS device does not fully meet the requirements of traditional Kalman filtering modeling in dynamic environment,resulting in limited filtering effect.Aiming at the problem that the system noise array and the measurement noise array semi-positive definiteness cause the filter instability in the adaptive filtering process,the corresponding optimization design is carried out.The results of dynamic and static experiments show that the Sage-Husa adaptive filtering based on improved time series modeling has significantly improved the accuracy of random error compensation compared with the traditional Kalman filtering.Thirdly,aiming at the problem of poor versatility of the current redundancy calibration scheme,the general error model of the redundant MEMS-IMU is established and a six-position calibration scheme is designed to realize the error parameter identification of the MEMS device under the number of arbitrary redundant configurations and the configuration structure.In order to further simplify the calibration procedure,shorten the calibration length and improve the calibration accuracy,a redundant MEMS-IMU calibration method based on Kalman filter is proposed.The error model is improved by the small angle rotation vector method to achieve accurate modeling of the installation error.Aiming at the problem that the output value of triaxial angular rate is used as an observation to cause partial error parameters,it is proposed to design the Kalman filter with the output error of the device as the observation.The error parameter is accurately estimated from the improvement of the measurement equation and the improvement of the error model.The simulation results show that the calibration scheme based on Kalman filter is significantly improved compared with the six-position calibration scheme,and the calibration time is greatly reduced.Finally,the laboratory four gyro redundancy system is taken as the research object,and the three-axis turntable is used as the testing platform.The redundant MEMS-IMU parameter identification and error compensation test is designed.The Experimental results show that the accuracy of the gyro measurement is greatly improved after the random error and the deterministic error compensation,and the effectiveness of the scheme is verified.