GPS数据处理中实时随机模型的估计方法

Real-time Stochastic Model Estimation in GPS Data Processing

为了满足在复杂观测条件下GPS基线解算的精度要求,研究了一种新的随机模型。与基于卫星高度角的随机模型难以定义出观测值之间的空间相关性不同,该随机模型核心是基于最小二乘估计得到的残差序列,通过移动窗口,利用前几个历元的残差序列,对当前历元观测值的方差-协方差阵进行实时估计。通过实例分析,将其与传统的随机模型的计算结果进行对比,验证了该实时随机模型在改善基线解算精度方面的有效性。

In order to meet GPS baseline solution accuracy requirements under complex observation conditions,this paper presents a study of a new stochastic model.The stochastic model based on satellite elevation angle is difficult to define a spatial correlation between different observations,and the core of the new stochastic model is the obtained residual sequence based on the least squares estimation by moving the window.Using the first epoch residual sequence,we estimate the current epoch observation variance-covariance matrix in real time.Test results show that the real-time stochastic model can improve the accuracy of baseline solution,compared with the traditional stochastic models.