基于正交变换的五阶容积卡尔曼滤波导航算法

In view of the problem that the high-order cubature Kalman filter has large high-order sampling error in high-dimension nonlinear systems, an orthogonal transformed fifth-order cubature Kalman filter (OT5-CKF) is proposed. Based on the orthogonality of the trigonometric function, the original cubature point is subjected to the appropriate volume transformation and brought into the Kalman filter model. The new cubature point can well avoid the high-order sampling error of high-dimension systems. Finally, the unmanned aerial vehicle integrated navigation system is used as the application background. The third-order CKF, fifth-order CKF and OT5-CKF are compared and analyzed. The results show that the filtering estimation accuracy of OT5-CKF is higher than others under the same observation conditions.