基于卡尔曼滤波方法的BDS动态伪距差分定位算法研究

针对北斗卫星导航系统(BDS)最小二乘伪距差分方法定位精度及稳定性不足的问题,提出了一种基于卡尔曼滤波方法的伪距差分算法,并进行了静态以及人行慢动态两种条件下的实验.对实测数据结果的处理分析表明,卡尔曼滤波方法在静态条件下,东、北、高三个方向精度分别提升55%、23%、48%.在动态条件下,东、北、高三个方向精度分别提升71%、49%、33%.

BDS dynamics based on Kalman filtering research on pseudorange differential positioning algorithm

Aiming at the problem of insufficient positioning accuracy and stability of BDS least squares pseudorange difference method, a new pseudorange difference algorithm based on Kalman filtering method is proposed. Experiments under static and human walk dynamic conditions are carried out. After analyzing the measured data, the results from Kalman filtering method improves the accuracy in the east, north and up directions by 55％, 23％ and 48％ respectively under static conditions. Under dynamic conditions, the accuracy in the east, north, and up directions increased by 71％, 49％, and 33％, respectively.