导航系统误差及其协方差阵的随机加权拟合

摘要

On the basis of adaptive fitting method which is based on the existing mobile window function model and the stochastic model system error, a random weighting fitting method for both the system errors and covariance matrices of model errors is presented by using moving windows. The random weighting estimation for covariance matrices of observations and the predicted states are given within the same window; the covariance matrices of the modified observations and the modified predicted states are also estimated. The observations and the predicted states are then modified. It is shown by the calculation and simulation results that the random weighting estimation algorithm can effectively resist the influence of the system errors on the estimated states of navigation, and the performance is superior to Kalman filtering and adaptive Kalman filtering.%在现有基于移动窗口函数模型和随机模型的系统误差自适应拟合方法的基础上,提出了一种基于移动窗口观测模型的系统误差随机加权拟合法.该方法在相同的窗口内给出了相应的观测向量协方差阵的随机加权估计.利用修正后的观测向量及相应的协方差阵进行导航滤波计算,能有效地抑制观测系统偏差的影响,提高导航解算的精度.仿真结果证明,文中所提出的随机加权估计算法的精度,明显优于卡尔曼滤波和自适应卡尔曼滤波.