高频动态多星座GNSS数据的周跳低时延解算方法

Low latency cycle slip determination for ultra-high sampling rate kinematic GNSS measurement

对于高频高动态的连续实时动态(RTK)定位,快速低时延的周跳探测与修复至关重要。基于高频数据误差特征和改进的几何无关相位组合MGF(modified geometry-free combination),本文提出了一种观测值域的动态GNSS周跳低时延解算方法OMGF(observation based MGF)。首先基于高频观测数据的噪声特性,利用伪距与相位组合得到周跳初始修复量,再结合MGF对初始修复量进行精确修复。推导了不同卫星系统的伪距和相位噪声水平对OMGF方法周跳修复成功率的影响,以及OMGF方法适用的观测值噪声边界条件。OMGF方法仅需利用单颗卫星的观测数据进行周跳探测与修复,且降低了周跳修复对伪距观测值的精度要求。利用实测的20 Hz三星座GNSS(GPS/BDS/GLONASS)动态数据进行试验分析,结果表明:OMGF方法的成功率约为99.998%;与GF(geometry-free combination)和HMW(Hatch-Melbourne-Wübbena combination)组合方法相比,OMGF方法解算速度提高了60倍。OMGF方法的算法复杂度低,计算效率高,有利于算力资源受限的移动端超高频实时动态定位数据的在线快速处理。

For high-speed and high-sampling-rate continuous real-time dynamic(RTK)positioning,fast cycle slip detection plays an important role.In this paper,based on the noise characteristics of ultra-high sampling rate data and the modified geometry-free(MGF)combination,a low-latency cycle slip determination method is proposed for kinematic GNSS data,named OMGF.Based on the noise characteristics of high sampling rate observation,cycle slips are preliminarily repaired by the pseudo-phase combination,and then MGF is used to perform secondary repair on the initial repair results.This paper deduces the success rate of OMGF cycle slip repair for pseudorange and carrier phase observations with different noise levels for different satellite systems,and the observation noise boundary conditions for OMGF method.OMGF method only needs the observation of a single satellite to detect and repair cycle slip,and extend the accuracy requirement of pseudorange observation for cycle slip determination.The experiment results with 20 Hz GPS/BDS/GLONASS measurements show that,the success rate of OMGF method is about 99.998%.Compared with the combination of GF and HMW,OMGF method is about 60 times faster.OMGF can perform cycle slip determination at low complexity and high calculation efficiency,which is beneficial for the mobile terminal with limited computing power resources to quickly process ultra-high sampling rate GNSS data online.