PCA空间滤波在高频GPS定位中的应用研究
Study on the Effect of PCA Spatial Filtering on High-rate GPS Positioning
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摘要: 利用主成分分析法(PCA)对高频GPS时间序列进行空间滤波,并结合Karhunen-Loeve展开法(KLE)对其结果进行判定,可有效地提取共模误差,提高单历元定位精度。通过对四川GPS连续观测网的计算分析表明,PCA方法可较好地减弱区域共模误差,并能准确地反映共模误差的空间分布,且精度优于传统的堆栈法,这对高频GPS技术的应用和发展具有重要意义。Abstract: In recent years,monitoring strong ground movement based on high-rate GPS continuous observing network has become a research focus in geo-scientific field.The spatial filtering we used is the principal component analysis(PCA) to extract the CME from the high-rate GPS position time series,and the Karhunen-Loeve expansion(KLE) is used to test the components.We process the high rate data from Sichuan GPS Continuous Observing Network,the results show that this method can eliminate the CME effectively,and the accuracy is slightly higher than the stacking.The key is that the spatial response of CME can be showed accurately by PCA,which is meaningful for applications and developments of the high rate GPS technology.