CEEMD在削弱GPS多路径效应误差中的研究分析

发布时间：2018-04-16 02:27

本文选题：GPS + 多路径效应　；参考：《东华理工大学》2017年硕士论文

【摘要】：相比于传统测量方法,GPS有着高精度,全天候观测,操作简便等诸多优势,因此被广泛地应用于变形监测领域。但是在GPS在采集数据的时候,由于环境,仪器和操作人员等因素,所采集的数据中不可避免的会掺杂一些噪声和多路径误差。这些误差的存在严重的影响观测的精度。本文就此开展研究与分析:(1)基于时频分析方法,对经验模态分解(EMD)重点分析,对其存在的问题进行了探讨,提出了总体模态分解(EEMD)和完全总体模态分解(CEEMD),通过仿真实验,表明CEEMD相比于EMD和EEMD在模态混叠和噪声残留的问题上有较大的改进。(2)通过实验比对了EMD,EEMD,和CEEMD三种滤波的去噪效果,结果表明CEEMD滤波去噪效果较好。介绍了基于CEEMD的小波阀值去噪,并与传统小波阀值去噪和CEEMD滤波去噪进行了对比,实验结果表明基于CEEMD的小波阀值去噪具有较好的去噪效果。并在此基础上提出了基于CEEMD的多方法融合去噪方法,并通过对比试验,表明该方法去噪具有一定的优势。并将这些方法引入到削弱多路径误差的应用中,实验表明,上述几种方法都具有一定的效果,其中基于CEEMD的多方法融合的效果最好。(3)基于CEEMD的多方法融合去噪和主成分分析(PCA)的组合修正多路径误差,PCA可将去噪后的连续多天的多路径误差的共性误差提取,用共性误差修正多路径误差。通过实测数据分析表明,该种组合方法较为适用,能提高定位精度。
[Abstract]:Compared with the traditional measurement method, GPS has many advantages, such as high precision, all-weather observation, simple operation and so on, so it is widely used in the field of deformation monitoring.But when GPS collects data, because of the environment, instrument and operator, it is inevitable that some noise and multipath error will be mixed in the data.The existence of these errors seriously affects the accuracy of observation.In this paper, based on time-frequency analysis method, this paper focuses on the analysis of empirical mode decomposition (EMD), discusses its existing problems, and puts forward the total mode decomposition (EEMD) and complete total mode decomposition (CEEMDN).The results show that compared with EMD and EEMD, CEEMD has a great improvement on modal aliasing and noise residue. The results show that CEEMD filter has better denoising effect than EMD and CEEMD filter.Wavelet threshold denoising based on CEEMD is introduced and compared with traditional wavelet threshold denoising and CEEMD filtering denoising. Experimental results show that wavelet threshold denoising based on CEEMD has better denoising effect.On this basis, a multi-method fusion denoising method based on CEEMD is proposed, and the comparison experiment shows that this method has some advantages.These methods are introduced into the application of reducing the multipath error. The experimental results show that the above methods are effective.Among them, multi-method fusion based on CEEMD has the best effect. PCA-based multi-method fusion denoising and principal component analysis (PCA) can extract the common error of multipath error of continuous and multi-day after de-noising.The multipath error is corrected by common error.The analysis of the measured data shows that the combined method is more suitable and can improve the positioning accuracy.
【学位授予单位】：东华理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：P228.4

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