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 |
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DOI: |
中文关键词: 北斗卫星导航 周跳 提升小波 动态神经网络 非差观测模型 |
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 |
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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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