摘 要：针对传统周跳检测和修复方法精度低的问题，基于 LM-BP 神经网络理论，以全球导航卫星系统（GNSS）的载波相位双差序列为输入数据集，提出了一种周跳检测和修复方法。实验结果表明：对于小周跳探测问题，基于双差检测量的LM-BP 神经网络探测修复法具有高敏感性，且相较于传统的多项式拟合法，新方法的周跳探测精度得到了提升。
关键词：周跳探测与修复；LM 算法；BP 神经网络；双差检测量；多项式拟合法
DOI:10.19850/j.cnki.2096-4706.2022.15.015
中图分类号：P228；TP18 文献标识码：A 文章编号：2096-4706（2022）15-0056-03
Research on Cycle Slip Detection and Repair of GNSS Based on Single Frequency Double Difference LM-BP Neural Network
LIANG Lingfeng
(School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China)
Abstract: In view of the low accuracy of traditional cycle slip detection and repair method, based on Levenberg Marquardt-Back Propagation (LM-BP) neural network theory, taking the carrier phase double difference sequence of Global Navigation Satellite System (GNSS) as an input dataset, a new cycle slip detection and repair method is proposed. The experimental results show that for the detection problem of small cycle slips, the LM-BP neural network detection and repair method based on double difference detection has high sensitivity. And compared with the traditional polynomial fitting method, the cycle slips detection accuracy of the new method is improved.
Keywords: cycle slip detection and repair; LM algorithm; BP neural network; double difference detection; polynomial fitting method
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作者简介：梁凌峰（1996—），男，汉族，河南焦作人，硕士研究生在读，主要研究方向：空间定位与导航技术。