摘要
针对由于多路径等因素的影响,采用GPS进行动态监测数据精度不能满足变形分析需要的问题,结合小波和经验模态分解(EMD)数据降噪方法,提出一种新的动态变形数据降噪模型EMD-Wavelet模型。将该模型用于动态坐标序列的降噪处理,通过小波方法对EMD分解的模态分量进行降噪,EMD对降噪后的模态函数进行重构,得到去噪后的坐标序列。与Wavelet、Kalman滤波、Kalman平滑和EMD相比较,EMD-Wavelet模型可以得到相对较高的信噪比和最小的均方根差、归一化绝对误差和偏差,表明EMD-Wavelet模型在GPS动态变形监测数据处理中相对较优。
Aiming at the problem that dynamic monitoring data accuracy from GPS can not meet the need of deformation analysis because of the multi-path and other factors, a new EMD-Wavelet dynamic deformation data denoising model through the combination of wavelet and EMD theory is proposed. Firstly, the model is presented to reduce noise of coordinate time series. Secondly, the modal components of EMD decomposition are de-noised with the wavelet model. Finally, the EMD reconstruction gives the extracted time series. Compared with the denosing models based on Wavelet, Kalman and EMD, the EMD-Wavelet model has relatively higher Signal-to-Noise Ratio（SNR） than other models and the lowest Root Mean-Square Error （RMSE） , ENAE and ERias with respect to the x/y/z coordinate time series. The results show that the EMD-Wavelet model has relative advantage in the data processing of GPS dynamic deformation monitoring.
出处
《大地测量与地球动力学》
CSCD
北大核心
2010年第5期77-80,85,共5页
Journal of Geodesy and Geodynamics
基金
地理空间信息工程国家测绘局重点实验室开放课题(200812)
关键词
EMD
小波变换
降噪模型
动态变形
数据处理
empirical mode decomposition （EMD）
wavelet transformation
noise reduction model
dynamic deformation
data processing
