自适应SCKF在高动态COMPASS信号参数估计中的应用

Application of Adaptive SCKF in Parameters Estimation of High Dynamic COMPASS Signal

高动态环境下北斗二号导航信号具有较高的非线性特性,载波参数估计难以保证较高的精度。在分析高阶非线性载波模型的基础上,提出了一种基于平方根容积卡尔曼滤波(SCKF)的自适应滤波算法,对载波相位及其三阶导数进行估计。该算法使用容积数值积分原则直接计算非线性随机函数的均值和方差,且在迭代滤波过程中,利用移动窗口法通过最新量测信息来改进过程噪声和量测噪声的协方差阵,可获得较高的估计精度。仿真结果表明,相比EKF和SCKF,本文提出的方法具有更高的估计精度和更快的收敛速度。

Due to the strong nonlinear characteristics of COMPASS signal under highly dynamic circumstances,the high accuracy parameter estimation is hard to be achieved.Based on the analysis of the high-order nonlinear carrier model,an adaptive square-root cubature Kalman filter algorithm（SCKF） is proposed to estimate the phase and its three-order derivatives.In the SCKF algorithm,cubature rule based on numerical integration method is directly used to calculate the mean and covariance of the nonlinear random function.By shifting the window,the latest measurement information in the process of recursion and filtering is used to improve the cross-covariance of noises,so the higher accuracy of state estimation can be achieved.The simulation results indicate that the higher accuracy and faster convergence are obtained compared with EKF and SCKF.