基于边缘采样的简化高阶CKF在非线性快速传递对准中的应用

针对舰载机惯导系统非线性传递对准问题中误差模型不完善的问题,同时考虑了挠曲运动和动态杆臂的影响,提出了一种新的适用于大方位失准角情形下的挠曲变形和杆臂效应加速度一体化误差模型。采用高阶容积卡尔曼滤波（HCKF）算法对状态进行滤波估计,考虑到HCKF具有较大的计算量,分析了传递对准模型的状态方程与量测方程结构,设计了一种基于边缘采样的简化高阶容积卡尔曼滤波（M-RHCKF）算法,其在时间更新中使用边缘采样算法,在量测更新过程中使用简化量测更新过程,并给出了该算法的证明过程。采用速度+姿态组合匹配方式,对提出的误差模型进行仿真实验。结果表明,该模型可以满足对准精度和对准时间的要求,相比于未考虑动态杆臂的传递对准模型具有更高的对准精度。

Application of marginal reduced high-degree cubature Kalman filter to nonlinear rapid transfer alignment

To solve the imperfection of error model during nonlinear transfer alignment in the inertial navigation system for shipboard aircraft, a new incorporate acceleration error model for lever-arm effect with large azimuth misalignment is presented with the flexure of a carrier. The High-degree Cubature Kalman Filter (HCKF) is used to estimate the state variables. Considering that HCKF requires a large amount of computation, the equation of state and measurement of the transfer alignment model is analyzed and a Marqunal Reduced High-degree Cubature Kalman Filter (M-RHCKF) algorithm is designed. The marginal sampling algorithm is used in the time update process and the reduced measurement update in the measurement update process. The proof of the algorithm is also given. The velocity plus attitude matching method is used to simulate the proposed model. The simulation results demonstrate that this model can meet the requirement of precision and time in transfer alignment. The misalignment estimation has a higher accuracy than the transfer alignment model without considering the dynamic lever-arm.