基于CDKF的飞机姿态角估计

针对扩展卡尔曼滤波(Extend Kalman Filter ,EKF)在飞机姿态估计中存在着计算复杂、线性化误差大等缺点,将一种基于Stirling内插公式的非线性滤波算法—中心差分卡尔曼滤波算法(Central Difference Kalman Filter, CDKF)应用于由低精度高噪声传感器组成的低成本飞机姿态估计系统中。首先建立基于四元数的飞机姿态数学模型,然后用CDKF方法进行姿态估计,并通过实测数据进行验证。实验结果表明, CDKF方法不仅有效地提高了飞机姿态估计的精度和稳定性；而且不需要模型的具体解析形式,避免了复杂的Jacobian矩阵的计算,算法更简单,也更容易实现,优于常用的EKF方法。

Aircraft Attitude Estimation Based on CDKF

When the Extend Kalman Filter (EKF) is applied in the aircraft attitude estimation, two defects exist: one is computational complexity; the other is large linearization error. Aiming at these problems, Central Difference Kalman Filter (CDKF) based on Stirling interpolation formulation is applied to the low-cost aircraft attitude estimation system which is of less accurate and high noisy sensors. First, the nonlinear mathematic model of aircraft attitude based on quaternion is established, then CDKF is used for attitude estimation.Experimental results with real flying data show that CDKF is superior to the commonly used EKF method. The algorithm not only improves the attitude estimation precision and stability greatly, but also avoids the computing burden of Jacobian matrices. In addition, it is more simple and easier to implement.