恶劣环境下GPS定位估计滤波算法的非线性模型

A Nonlinear Model for GPS Positioning Estimation Using Filter Algorithms under Bad Conditions

提出了一种附加测量模型用于GPS定位估计的系统方程,该方法可实现单机GPS在可捕获卫星数较少的恶劣环境下的定位.开发了一种新的基于非线性滤波的位置和速度估计模型,在测量模型中引入随位置变化的电离层误差补偿模型,利用平淡卡尔曼滤波(UKF)算法较好地实现了可见卫星为3颗条件下的定位估计.在仅有2颗卫星可见的情况下,进行了把高程加入测量模型做平面定位估计的尝试,并取得了较好的效果.GPS定位实验结果表明,在恶劣环境下,基于所提出的非线性模型得出的滤波定位估计值能达到理想的精度.

This paper presented an additive measurement equation to GPS position estimation system equations. This method can be used in standalone GPS receiver when it catches fewer satellites under bad conditions. A new model for position and velocity estimation was developed based on nonlinear filtering. This model includes an ionospheric delay correct model which verifies with the position. Using unscented Kalman filter （UKF） algorithm implements the position estimation under the condition of only three received satellites. Adding the height equation to the measurement equation implements the 2-D position estimation when only two received satellites. The experimental results show that the position estimation result obtained with this new nonlinear model using UKF can reach ideal accuracy.