基于EMD-LSTM耦合预测模型的BDS多路径误差削弱方法研究

徐小汶; 陶 远

doi:DOI:10.13442/j.gnss.1008-9268.2020.02.016

基于EMD-LSTM耦合预测模型的BDS多路径误差削弱方法研究

徐小汶,
陶远

安徽理工大学测绘学院,安徽淮南 232001

详细信息

作者简介:
徐小汶 (1991—),男,硕士,研究方向为GNSS卫星导航与定位.

通信作者:
徐小汶 E-mail:2399472552@qq.com

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出版历程
- 刊出日期: 2020-04-14

BDS multipath errors reducing method based on EMD-LSTM coupled prediction model

School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China

摘要

摘要: 北斗卫星导航系统(BDS)在短基线测量中存在的多路径误差是影响定位精度的主要误差项. 针对多路径误差的非线性以及坐标序列的非平稳特性,拟采用经验模态分解(EMD)与长短期记忆网络(LSTM)结合的方法,构建EMD-LSTM耦合预测模型,对多路径误差进行预测,削弱多路径误差的影响. 实验结果表明,EMD-LSTM耦合预测模型能够有效地削弱了多路径误差影响,E、N、U方向精度分别提高了22％、36％、40％.
- 北斗导航卫星系统 /
- 多路径误差 /
- 经验模态分解 /
- 长短期记忆网络
Abstract: The multipath errors in short baseline measurement of BeiDou Navigation Satellite System (BDS) were the main errors affecting positioning accuracy. Aiming at the nonlinearity of multipath errors and the non-stationarity of coordinate series, a coupled prediction model of EMD-LSTM was proposed by combining empirical mode decomposition (EMD) with long short-term memory (LSTM) to predict multipath errors and weaken the influence of multipath errors. The experimental results show that the EMD-LSTM coupled prediction model can effectively reduce the multipath errors, and the E, N,and U directions were respectively improved by 22％, 36％, and 40％.
- BeiDou navigation satellite system /
- multipath errors /
- empirical mode decomposition /
- long short-term memory

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