导航解算中的有色噪声及其协方差矩阵自适应拟合

使用Kalman滤波进行动态导航定位解算需要涉及函数模型和随机模型, 而在实际应用中, 精确的函数模型和随机模型很难直接给出, 因此, 动态Kalman滤波的精度和可靠性将会受到函数模型误差和随机模型误差的影响. 在假设观测噪声和动力学模型噪声主要是具有一阶自相关特性的有色噪声的基础上, 提出了一种基于移动窗口的有色噪声函数模型和随机模型的自适应拟合法. 给出了计算有色噪声估值和噪声协方差矩阵的表达式, 并利用实测数据验证了模型及算法的可行性和实用性. 计算结果表明, 该算法能有效抵制有色噪声对导航滤波结果的影响. 

Adaptive Fitting of Colored Noises and Corresponding Covariance Matrices in Navigation

To use Kalman filtering for kinematic navigation and positioning, we have to deal with function model and stochastic model. The precision and reliability of kinematic Kalman filtering are affected remarkably from the function model and stochastic model errors. Adaptive fitting of both colored noise and covariance matrices by using moving windows are presented based on the assumption that the observation and dynamic model noises mainly include the colored noises with first order self-correlation character. The expressions to calculate the colored noise estimators and covariance matrices of the modified observations and predicted states are obtained. Feasibility and practicability of the model and algorithm are tested by an example. It is shown that the Kalman filtering, based on the adaptive fittings of the colored noises and covariance matrices, can be effective in resisting the influence of the colored noises on the navigation results.