在高斯滤波框架下，阶次越高，近似精度越高。为提高滤波精度，通过提高阶次，提出了七阶正交容积卡尔曼滤波（CQKF）算法。在传统CQKF算法的基础上，该算法扩展了线性积分的近似阶次，提出了七阶球面积分的确定性采样方法；进而扩展了球-半径准则，提高了滤波估计精度。飞行器目标跟踪的仿真实验证明了该算法的有效性，证明了七阶CQKF比五阶CQKF、三阶容积卡尔曼滤波器（CKF）和无迹卡尔曼滤波器（UKF）有更高的滤波精度。
In the Gaussian filter frame, the higher the order, the higher the accuracy of the approximation. To improve the filtering accuracy, a seventh-degree Cubature Quadrature Kalman Filter (CQKF) algorithm is proposed by improving the degree. Based on the traditional CQKF, the algorithm extends the approximate degree of linear integrals, and proposes a deterministic sampling method for the seven-degree spherical integral. Then the spherical-radial rule is extended to improve the accuracy of the filter. The simulation results of the aircraft target tracking demonstrate the effectiveness of the algorithm. It is proved that the seventh-degree CQKF is more accurate than the fifth-CQKF, third-degree Cubature Kalman Filter (CKF) and Unscented Kalman Filter (UKF).
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