预测-五阶容积卡尔曼滤波方法

Predictive fifth-degree cubature Kalman filter method

为适用于强非线性、非高斯过程噪声系统,结合预测滤波(PF)与高阶容积卡尔曼滤波(HCKF),提出一种预测-五阶容积卡尔曼滤波(P5thCKF)方法。通过预测滤波方法对系统模型中的过程噪声及其方差阵进行实时调整,进而将新模型代入到五阶容积卡尔曼滤波框架中进行实时递推状态估计。推导了五阶球面单形-径向积分准则,采用五阶球面单形积分准则处理球面积分,广义高斯-拉盖尔积分准则处理径向积分;描述了预测滤波方法并对模型误差调整量进行了推导。通过2个仿真实验验证了本文方法在强非线性、非高斯过程噪声系统中的可行性以及应用于工程实践的可能性。

A Predictive fifth-degree Cubature Kalman Filter(P5thCKF)method,which combines the Predictive Filter(PF)and the High-degree Cubature Kalman Filter(HCKF)is proposed for strongly nonlinear and non-Gaussian process noise systems.The PF is used to adjust the process noise and variance matrix in the system model in real time,and then the new model is put into the fifth-degree cubature Kalman filter framework to perform real-time recursive state estimation.The fifth-degree spherical simplex-radial rule is derived and is used to deal with spherical integration,and the generalized Gauss-Laguerre integral rule is used to deal with radial integration.The predictive filtering method is described,and the error adjustment amount of the model derived.The feasibility of the proposed method in strongly nonlinear and non-Gaussian process noise systems and its possible application to engineering practice are verified by two simulation experiments.