Precise Calibration of Projectile-Borne MEMS Gyroscopes Based on the EWMA-DD Method

Calibrating micro-electromechanical systems (MEMS) gyroscopes can significantly improve the accuracy of projectile attitude perception in highly dynamic and thermally shocked environments. Gyroscopes calibrated using mainstream least squares (LS) methods experience significant performance degradation in terms of accuracy, while intelligent algorithms are unsuitable for projectile environments due to their “black box” nature and implementation complexity. To address the existing limitations, this article presents a novel sensor calibration method based on dendritic neural networks (DDs) and exponentially weighted moving average (EWMA) algorithms for gyroscopes in a projectile environment. The method combines the advantages of model-driven and data-driven techniques. First, a three-axis gyroscope calibration model was developed for highly dynamic flight environments. By introducing temperature terms, bias terms, and corresponding composite terms, the model accurately quantifies the effects of high rotation and sudden temperature changes on the gyroscope output. Second, the input-output recognition capability of DDs is innovatively utilized to solve the complex coefficients in the model. Finally, the EWMA algorithm is applied to assign weights to data points, suppressing low-weight noise points. Semi-physical simulations were conducted on a three-axis temperature-controlled turntable and a high-speed flight simulation turntable. The experimental results demonstrate that this method effectively suppresses sensor zero drift caused by temperature, reduces noise and outliers in the sensor output, and enhances the accuracy of the sensor output. Compared to high-order LS and long short-term memory (LSTM) algorithms, the root mean square error (RMSE) value of the sensor output calibrated by this method improved by 40.7% and 19.9%, respectively.