A novel H∞ filter called square-root cubature H∞ Kalman filter is proposed for attitude measurement of high-spinning aircraft. In this method, a combined measurement model of three-axis geomagnetic sensor and gyroscope is used, and the Euler angle algorithm model is used to reduce the state dimension and linearize the state equation, which can reduce the amount of calculation. Simultaneously, the method can be applied to the case of measurement noise uncertainty. By continuously modifying the error limiting parameters to update the measurement noise estimation, the filtering accuracy and robustness can be improved. The square-root forms enjoy a consistently improved numerical stability because all the resulting covariance matrices by QR decomposition are guaranteed to stay positive semidefinite. The algorithm is applied to the simulation experiment of attitude measurement with the combination of geomagnetic sensor and gyroscope and compared with the results of Unscented Kalman filter, cubature Kalman filter, square root cubature Kalman filter, and singular value decomposition cubature Kalman filter, which proves the effectiveness and superiority of the algorithm.

中文翻译：

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基于H∞滤波器的平方根Couture卡尔曼滤波在高旋翼飞机姿态测量中的应用

提出了一种新颖的H∞滤波器，称为平方根菌落H∞卡尔曼滤波器，用于高旋转飞机的姿态测量。该方法采用了三轴地磁传感器与陀螺仪的组合测量模型，并使用欧拉角算法模型减小了状态维并使状态方程线性化，从而减少了计算量。同时，该方法可以应用于测量噪声不确定性的情况。通过连续修改误差限制参数以更新测量噪声估计，可以提高滤波精度和鲁棒性。平方根形式的数值稳定性一直得到改善，因为所有由QR分解得到的协方差矩阵都保证保持正半定值。