摘要： 为解决高精度北斗卫星导航系统定位中小周跳难以探测与修复的问题,提出一种基于改进局部均值分解的单频周跳探测与修复方法。该方法利用伪距和载波相位观测值构造周跳检测量,改进局部均值的分解处理,获得若干个乘积函数分量,根据瞬时幅值函数极大值点的位置探测出周跳发生的历元,运用最小二乘支持向量机对周跳发生前分量的时间序列建立预测模型,通过比较实测值与预测值的大小来修复周跳。应用实测的观测数据对算法进行验证,结果表明,与小波分析法相比,该方法无小波函数选取的难题,可以对单频小周跳进行准确探测与修复。

关键词: 单频, 小周跳, 周跳探测与修复, 周跳检测量, 局部均值分解, 最小二乘支持向量机

Abstract: A method to detect and restore single-frequency cycle-slips based on improved Local Mean Decomposition(LMD) is proposed to solve the problem that the small cycle-slip is difficult to detect and restore by high accuracy Beidou navigation satellite System(BDS) location.This method constructs the cycle-slip detectable quantity by observation value of the pseudorange and carrier-phase,and decomposes it by LMD to get a certain number of Product Function(PF) components,and the epoch where cycle-slip appears is detected accurately according to the location of the maximum point of instantaneous amplitude function.The forecasting model for time series of PF components before cycle-slip appears is established by applying the Least Squares Support Vector Machine(LS-SVM) and the cycle-slip is restored by comparing the measured value with predicted value.This method is verified by using the observation value detected,result shows that this method has no problem that wavelet function is hard to select compared with Wavelet analysis and it can detect and restore the single-frequency cycle-slips accurately.

Key words: single-frequency, small cycle-slip, cycle-slip detection and correction, cycle-slip detectable quantity, Local Mean Decomposition(LMD), Least Squares Support Vector Machine(LS-SVM)

中图分类号: 

• TP391