基于非线性卡尔曼平滑的GPS定位估计方法

在GPS单机定位中,通常采用卡尔曼滤波作为位置状态解算的方法.文中提出一种将非线性平滑技术用于GPS定位估计的方法,该方法可用于单机GPS接收机的定位解算,在非线性滤波的基础上进一步提高定位精度.提出一种随接收卫星数量而实时改变测量参数的动态测量模型,根据GPS的伪距、多普勒频移和导航信息等原始数据进行定位模型的解析,运用新型的平淡卡尔曼平滑算法求解该动态模型.GPS定位实验结果表明,与通用的最小二乘迭代法和非线性滤波等方法获得的结果相比,所提出的方法能获得更高的定位精度.

A Nonlinear Kalman Smoothing Algorithm for Improving GPS Positioning Precision

In Stand-Alone GPS positioning,the Kalman filter is used as a general state estimation method.This paper presented a method of nonlinear smoothing techniques for GPS based position estimation.This technique can be used in stand-alone GPS receiver with higher precision compared with nonlinear filtering.A nonlinear measurement model was proposed,which has variable measurement number for coping with an arbitrary number of satellites.This nonlinear model was analyzed using GPS raw data,pseudorange and Doppler shifts measurements.The model was investigated for applying it to a new nonlinear estimation algorithm: Unscented Kalman Smoothing.The experimental results were compared with the estimation results obtained by the filtering model using nonlinear estimation technique.It shows that the proposed method has higher positioning precision.