共查询到20条相似文献，搜索用时 562 毫秒
1.
?????????????糡?????????????????????塢???????????????????3???????糡??????????????????о????????????1?????????????糡???????????н???????????????????仯????????????С???????????????λ?????2?????????????糡??????????????????????????????????????????????????????????????????????о??? 相似文献
2.
???????GPS??GRACE?????????????α???????????????????GRACE???λ???GPS?????????????о?????(RMS)??С?????????????仯????λ???????????????н???????????GPS?????????????仯?????????????????????????????Щ???????????????????????????С?????????????????????????????????????п??????????????????????ó???GRACE?????α????????С????????????????????????????????????????????????GPS??????????????????д??μ??????????????????GPS???????????????о??? 相似文献
3.
��ǰ�����TEC�쳣̽�ⷽ���о� 总被引：4，自引：3，他引：1
?????????????TEC??????????(??????????????????λ?????????λ????)???????????????????TEC????????в??????TEC?????????????TEC??????н????????????????????????????????????й??????????????GNSS???????????????TEC?????????????????????8.0?????????з????????????÷????????????Ч?????????????TEC????????? 相似文献
4.
???????в?????????????г????????????????TEC???????????????????????14??IGS??????15?????????????VTEC?????????????PPP????С???CODE?????ж???VTEC???????2TECU??VTEC?????????95%????1TECU????????????PPP????У????????λ?????????????17%??30%??????????????15%??30%?????? 相似文献
5.
?о???????????????????????????????????????????????????????????????????????????????????Ч??????????????????????????????λ????????????? ???????2??????????λ???????????????????????λ???????, ?????????????DEM?????????????????????????????? 相似文献
6.
?????????????????????????????????????????????????????????????б?????????????LIM???????Ч?????????????С??100 km?????徫??????3 cm?? 相似文献
7.
?????????????????????λ?????????????·?????????????С???????????????÷????????鷨????????????????????????е???λ????????????????????????????????????￡???????????????????????????????????????????????????????????????÷???????Ч??? 相似文献
8.
????ROI_PAC????????????????ALOS PALSAR 474?????4?????????ERA-Interim?????NeQuick?????????????ж?????????????????????????????λ???????5??GPS?????????????????б????????????????????????????D-InSAR???ó?????λ????侭????????????????????????λ?????GPS??????????????????0.104 m?????????????0.057 m,???????????????????????????????D-InSAR????????????? 相似文献
9.
????3?????????????????LiDAR??????????????????????????LiDAR??????????????飬????????????????????????????????????????С???巽???????????3??????????????????3?????????????3???????????????????????????裬?????????????μó?????????б????????????????????????????????????????????????????????????????й???????????У?????????????в??????С??detMCD?в????????????С????????????????????????????????????????????????????????????????????????????????????С???????????????????????????????????????TIN?????????????,???????????????????????????Ч???LiDAR?????????е???????????????????????? 相似文献
10.
?????????????λ??????????????????????????????????t??????????????????????????????????????????????????λ?????GPS????????????????????????????mm/s?????????????1 cm/s?? 相似文献
11.
RTK-GPS�����������Ķ�Ƶ�ʳɷַ��� 总被引：2，自引：1，他引：1
???????RTK-GPS(????????λ??)???????????????????????????????????????????????????????RTK-GPS???????????????????????????????????????????????????????????е?? 相似文献
12.
《世界地质(英文版)》2015,(3)
The ground penetrating radar( GPR) detection data is a wide band signal,always disturbed by some noise,such as ambient random noise and multiple reflection waves. The noise affects the target identification of underground medium seriously. A method based on principal component analysis( PCA) was proposed to extract the target signal and remove the uncorrelated noise. According to the correlation of signal,the authors get the eigenvalues and corresponding eigenvectors by decomposing the covariance matrix of GPR data and make linear transformation for the GPR data to get the principal components( PCs). The lower-order PCs stand for the strong correlated target signals of the raw data,and the higher-order ones present the uncorrelated noise.Thus the authors can extract the target signal and filter uncorrelated noise effectively by the PCA. This method was demonstrated on real ultra-wideband through-wall radar data and simulated GPR data. Both of the results show that the PCA method can effectively extract the GPR target signal and remove the uncorrelated noise. 相似文献
13.
陶国强 《大地测量与地球动力学》2021,41(12):1223-1229
考虑到传统谐波模型难以精确描述GNSS坐标时间序列的非线性变化,导致信号和噪声不能很好地分离,进一步影响粗差探测和噪声估计,本文提出一种基于奇异谱分析的粗差探测与噪声估计算法。首先采用奇异谱分析方法分离出GNSS坐标时间序列中的信号与噪声,然后基于IQR准则探测噪声中的粗差,最后采用最小二乘方差分量估计（LS_VCE）方法定量估计各噪声分量。算例表明,相比于传统基于谐波模型的算法,该算法的粗差探测准确率更高,且估计的噪声分量与真值更接近。 相似文献
14.
针对主成分分析法提取时变重力场模型信号中存在信号泄漏和去条带噪声不明显的问题，提出对球谐位系数主成分分析的改进方法。首先对球谐位系数进行主成分分析，提取主要的模态，再对不同模态根据其自身噪声特点选择合适的滤波方法和参数，最后在此基础上进行信号提取。以CSR发布的2005-01~2013-12(108个月)GRACE时变地球重力场模型进行实验，提取2006-06和2006-12等效水高数据和改进前的主成分分析进行比较，表明此方法在有效地剔除条带误差的同时又使得信号泄漏减小。 相似文献
15.
为了更好地消除混杂在变形序列中的噪声,利用完备经验模态分解（CEEMD）将形变信号自适应分解为不同尺度的振动模态。针对分解分量中信号和噪声区分标准不唯一的问题,构造一种CEEMD与自相关分析相结合的去噪算法,实现有效信号和随机信号的分离。将该算法应用在仿真实验和GNSS变形监测实测数据处理中,并与传统的小波去噪方法进行比较。结果表明,该算法避免了小波基选择带来的影响。 相似文献
16.
???????????????SSA????С??????????????????????????????Ρ???????????????????????????????е?????????С?????????????????????????????????????????????????Ч??????????????????????????????ξ??ж????????????????????X??Y????????????????????????????￡??????????г???? 相似文献
17.
将ICA（Independence Component Analysis）消噪原理应用于合成孔径雷达抗噪声干扰技术中,用噪声信号和受到干扰的SAR回波数据作为扩展的虚拟观测信号,对扩展的多维加噪观测信号进行分离, 得到源雷达回波信号, 从而实现噪声的有效消除.通过对条带式SAR点目标成像进行仿真试验, 结果表明这种消噪方法在消除SAR噪声干扰中是有效的. 相似文献
18.
基于改进谱减算法的语音增强研究 总被引：3，自引：0，他引：3
介绍了一种根据传统的谱减改进后的增强算法：根据每帧的功率谱来动态调整谱减系数,然后采用多种方法抑制音乐噪声.使谱减效果既保持了较高的信噪比,又使音乐噪声得到了抑制.算法在VC6.0下编程实现,实验证明语音增强效果明显. 相似文献
19.
针对监测结果受测量噪声和多路径等GPS误差影响的问题,提出基于PCA及提升小波的组合算法来提取建筑物结构振动信号。利用PCA空间滤波分离区域站点相关的共模误差,然后利用提升小波变换对振动信号进行降噪,用于提取结构振动信号。以香港某高楼在台风荷载作用下的观测数据为例进行实验,结果表明,此算法有效提高了变形监测的精度。 相似文献
20.
Remote sensing data have been widely applied to extract minerals in geologic exploration, however, in areas covered by vegetation, extracted mineral information has mostly been small targets bearing little information. In this paper, we present a new method for mineral extraction aimed at solving the difficulty of mineral identification in vegetation covered areas. The method selected six sets of spectral difference coupling between soil and plant (SVSCD). These sets have the same vegetation spectra reflectance and a maximum different reflectance of soil and mineral spectra from Hyperion image based on spectral reflectance characteristics of measured spectra. The central wavelengths of the six, selected band pairs were 2314 and 701 nm, 1699 and 721 nm, 1336 and 742 nm, 2203 and 681 nm, 2183 and 671 nm, and 2072 and 548 nm. Each data set’s reflectance was used to calculate the difference value. After band difference calculation, vegetation information was suppressed and mineral abnormal information was enhanced compared to the scatter plot of original band. Six spectral difference couplings, after vegetation inhibition, were arranged in a new data set that requires two components that have the largest eigenvalue difference from principal component analysis (PCA). The spatial geometric structure features of PC1 and PC2 was used to identify altered minerals by spectral feature fitting (SFF). The collecting rocks from the 10 points that were selected in the concentration of mineral extraction were analyzed under a high-resolution microscope to identify metal minerals and nonmetallic minerals. Results indicated that the extracted minerals were well matched with the verified samples, especially with the sample 2, 4, 5 and 8. It demonstrated that the method can effectively detect altered minerals in vegetation covered area in Hyperion image. 相似文献