MEMS陀螺仪随机误差KF补偿方法的普适性验证

针对MEMS陀螺仪随机误差成为影响系统导航精度的主要因素及建模存在的普适性等问题，提出一种3次卡尔曼迭代估计的误差补偿方法。采用Allan方差对MEMS陀螺仪随机误差参数进行辨识，同时结合MEMS陀螺仪输出数据自相关与偏相关函数具备的拖尾性，设计基于时间序列ARMA的MEMS陀螺仪误差卡尔曼滤波模型。采用同型号6只MEMS陀螺仪惯性测量单元搭建测试环境，并开展导航验证实验，结果表明，经过滤波处理后的MEMS陀螺仪各项误差系数明显降低，且滤波效果随滤波次数的增加而增强。6组MEMS陀螺仪测试设备显现出的误差减弱趋势具有一致性，姿态误差对比结果也进一步验证了该方法的可行性。

Universality Verification of Random Error Compensation Method for MEMS Gyroscope with Kalman Filter

The random error of MEMS gyroscope has become the main factor affecting system navigation accuracy, and there is a universality problem in modeling, so we propose an error compensation method of cubic Kalman iterative estimation. The Allan variance is adopted to identify the random error parameters of MEMS gyroscope. Combining the trailing property of MEMS gyroscope output data autocorrelation and partial correlation function, we design a Kalman filter model of MEMS gyroscope error based on time series ARMA. The test environment is set up with 6 MEMS inertial measurement units of the same model, and the verification experiment is carried out. The results show that the error coefficients of the MEMS gyroscope after the filtering process are significantly reduced, and the filtering effect is enhanced with the increased filtering. Meanwhile, the 6 sets of MEMS test equipment show the consistency of the error reduction trend. The comparison of results of attitude errors caused by gyroscope show that the filter method proposed in this paper is helpful for navigation.