基于自适应衰减因子Kalman 滤波的GPS 相位平滑伪距算法

载波相位平滑伪距的主要目的是通过高精度的载波相位测量值作为辅助量,使伪距测量值中、大随机误差得以消减。针对GPS 伪距测量中未知时变的噪声,提出基于极大后验时变噪声统计估计器的自适应衰减因子Kalman 滤波算法(AFKF),采用衰减的加权因子,使估计器逐渐忘记陈旧数据的作用,同时增加新数据的比重,避免滤波过程的发散。结合载波相位平滑伪距原理,利用AFKF 算法对全球导航卫星系统(GNSS)的国际GNSS 服务组织(IGS)的跟踪站实测数据进行仿真分析,并提出利用伪距双差值及伪距三差值来直观体现不同算法的效果比较,结果表明:与标准Kalman 滤波相比,AFKF 算法在伪距平滑应用中取得很好的效果。

Algorithm of GPS phase smoothing pseudo-range based on adaptive attenuation factor Kalman filtering

The main purpose of carrier phase smoothing pseudo-range is to reduce large random error of pseudo-range measurement values, by using high-precision carrier phase measurement values as the supplementary information. In view of the unknown time-varying noise in GPS pseudo-range measurement, an algorithm of adaptive attenuation factor kalman filter (AFKF) was put forward, which was based on maximum a posteriori (MAP) time-varying noise statistical estimator. In order to avoid the divergence of filtering process, the effect of old data could be gradually forgotten by using estimator with attenuation weighted factors, while the proportion of new data could be increased. Simulation analysis was carried out on the measured data of tracking station of a International Global Navigation Satellite System Service (IGS), by using the AFKF algorithm combining with carrier phase smoothing pseudo-range principle. And the double differential and the three differential pseudo-ranges were proposed to intuitively reflect the effects of different algorithms. Experimental results show that the AFKF algorithm can obtain better effect in application of pseudo-range smoothing, compared with the standard KF algorithm.