电子测量与仪器学报

刘春,葸生宝,李维华,蒋文钢,陈豪,何敏.基于 NAR 动态神经网络的 BDS 周跳探测与修复方法[J].电子测量与仪器学报,2021,35(7):36-43

基于 NAR 动态神经网络的 BDS 周跳探测与修复方法

BDS cycle slip detection and repair method basedon NAR dynamic neural network

DOI：

英文关键词:Beidou satellite navigation cycle slip lifting wavelet dynamic neural network non-difference observation model

基金项目:合肥市北斗卫星导航重大应用示范项目资助

作者	单位
刘春	1. 合肥工业大学电气与自动化工程学院
葸生宝	1. 合肥工业大学电气与自动化工程学院
李维华	1. 合肥工业大学电气与自动化工程学院
蒋文钢	2. 黄山风景区管理委员会
陈豪	1. 合肥工业大学电气与自动化工程学院
何敏	1. 合肥工业大学电气与自动化工程学院

Author	Institution
Liu Chun	1. School of Electrical and Automation Engineering, Hefei University of Technology
Xi Shengbao	1. School of Electrical and Automation Engineering, Hefei University of Technology
Li Weihua	1. School of Electrical and Automation Engineering, Hefei University of Technology
Jiang Wengang	2. Huangshan Scenic Area Management Committee
Chen Hao	1. School of Electrical and Automation Engineering, Hefei University of Technology
He Min	1. School of Electrical and Automation Engineering, Hefei University of Technology

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中文摘要:

针对北斗导航定位系统(BDS)数据处理过程中出现的周跳问题,提出一种提升小波结合 NAR 动态神经网络的周跳探测与修复方法。首先构造了非差周跳检验量,通过提升小波法探测到周跳发生历元,再采用 NAR 动态神经网络法、改进 BP 神经网络法以及传统多项式拟合法,分析对比不同方法周跳修复效果。实验仿真结果表明,在周跳探测方面,提升小波法可有效探测 0. 2 周以上的小周跳;在周跳修复方面,NAR 神经网络比改进 BP 神经网络的拟合度提高 40%左右,预测精度比改进的 BP 神经网络提高 50%左右,比传统多项式拟合法提高 10%以上,更适用于小周跳的探测与修复,进一步提高了定位精度。

英文摘要:

Aiming at the cycle slip problem in the data processing of the Beidou navigation and positioning system (BDS), a method for detecting and repairing cycle slips based on lifting wavelet combined with NAR dynamic neural network is proposed. Firstly, the nondifference cycle slip test quantity is constructed, and the epoch of cycle slip is detected by the lifting wavelet method. Then, the NAR dynamic neural network method, the improved BP neural network method and the traditional polynomial fitting method are used to analyze and compare the effect of different methods on cycle slip repair. Experimental simulation results show that in cycle slip detection, the lifting wavelet method can effectively detect small cycle slips of more than 0. 2 weeks; in cycle slip repair, the NAR neural network improves the fit of the improved BP neural network by about 40%, and the prediction accuracy is about 50% higher than the improved BP neural network, and more than 10% higher than the traditional polynomial fitting method. It is more suitable for the detection and repair of small cycle slips, and further improves the positioning accuracy.

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