基于容积卡尔曼滤波的飞机姿态估计方法

Aircraft attitude estimation method based on CKF

为了提高非线性模型下飞机姿态估计的精度,建立了基于四元数与低精度高噪声传感器的飞机姿态估计模型,应用基于球面径向积分准则的容积卡尔曼滤波算法进行姿态估计,通过实测数据进行模型与算法验证,并与扩展卡尔曼滤波算法和中心差分卡尔曼滤波算法估计结果进行了比较。对比结果表明:采用容积卡尔曼滤波算法能够有效提高飞机姿态估计的精度和稳定性,估计误差最小,估计时间最短,而且,在运算过程中无需求导与可调参数。

In order to improve the estimation accuracy of aircraft attitude in nonlinear model, a nonlinear estimation model based on the quaternion and the sensors with less accuracy and higher noise was established, and the cubature Kalman filter（CKF） algorithm based on spherical-radial cubature rules was applied to estimate the attitude. The model and algorithm were verified by using the real flying data. The estimation results of cubature Kalman filter algorithm, extended Kalman filter （EKF） algorithm and central difference Kalman filter （CDKF） algorithm were compared. Comparison result indicates that the estimation precision and stability of CKF are higher, its estimation time and error are minimum, and the derivation and adjustable parameters are not required in the estimation. 1 tab, 7 figs, 15 refs.