共查询到18条相似文献,搜索用时 187 毫秒
1.
《测绘科学》2020,(8)
为了滤除变形数据中含有的白噪声,该文提出一种基于粒子群优化算法的双重变分模态分解-小波阈值去噪模型。首先利用VMD对变形数据进行初次分解,初次分解层数K_1由频谱图波峰个数确定,根据相关性分析将分量分为噪声分量和信号分量;然后针对信号分量出现模态混叠的现象,首次分解的信号分量再次进行粒子群优化的VMD分解,得到二次信号分量和二次噪声分量;对二次VMD分解得到的噪声分量进行小波阈值降噪;最后重构实现噪声的有效剔除。模拟实验结果显示,利用本文方法去噪得到的均方根误差降低至0.418 0 mm、信噪比提升至10.174 0 dB,对比小波阈值、总体经验模态分解(EEMD)、VMD等方法,降噪效果有明显的提升。在实际变形数据去噪中,相比于其他去噪方法,本文方法能够很好地抑制模态混叠的现象,且均方根误差降低至0.151 0 mm、信噪比提升至23.821 0 dB,验证了本文方法在实际应用中的有效性。 相似文献
2.
为了精确剔除全球导航卫星系统(global navigation satellite system, GNSS)坐标时间序列中的噪声,提出一种联合遗传算法(genetic algorithm, GA)和变分模态分解(variational mode decomposition, VMD)的降噪方法 GA-VMD。该方法首先利用GA优化VMD参数,然后引入多尺度排列熵(multi-scale permutation entropy, MPE)作为噪声分量的筛选标准,最后将剩余分量重构得到降噪后的信号。通过仿真信号和实测数据的降噪实例,并与小波降噪(wavelet denoising, WD)、经验模态分解(empirical mode decomposition, EMD)等方法对比,分析GA-VMD的降噪效果。实验结果表明:对于仿真信号而言,GA-VMD方法相较于WD、EMD方法,信噪比分别提高了5.18 dB和2.91 dB,互相关系数分别提高了0.05和0.02;对于实测数据而言,GA-VMD方法对测站的速度不确定度和闪烁噪声的平均改正率分别为79.89%和84.46%,优于其他两... 相似文献
3.
在全波形激光雷达信号从发射到接收的过程中,针对受传播介质、扫测距离、物体性质等因素影响产生噪声的问题,本文提出了一种基于经验模态分解、排序熵和小波阈值的降噪改进方法。首先对波形信号进行经验模态分解或本征模态函数(IMF),计算各本征模态函数排序熵的值;然后应用该值计算小波阈值,并构造新的小波阈值函数,再对相应本征模态函数进行小波阈值降噪;最后将结果重新加和,得到降噪后的波形,从而提高不同噪声信号的降噪方法的自适应性。基于数值仿真和实测数据试验,将本文方法与其他降噪方法进行了对比,基于信噪比、波形相关性、均方根误差、平滑度计算归一化指标和综合指标对本文方法进行了评估,归一化信噪比提高10%~20%,其余指标改善5%~40%。因此,本文方法对不同噪声含量的回波信号均有较好的降噪效果,可以解决全波形激光雷达接收波形中存在的噪声问题。 相似文献
4.
为了有效地提取GNSS站坐标时间序列的有用信息,降低噪声干扰,本文提出一种局部均值分解和奇异值分解相结合的信号降噪方法,并利用5个测站的实测坐标时间序列对新方法进行了验证。首先通过局部均值分解将坐标时间序列分解成一系列PF分量和余项,然后利用连续均方误差方法确定高频分量与低频分量的分界点,保持低频分量不变,运用奇异值分解方法对高频分量进行降噪重构,最后将重构的高频分量与低频分量叠加得到最终的降噪坐标时间序列,并对降噪效果进行对比分析。结果表明,与单纯的奇异值分解方法相比,局部均值分解和奇异值分解相结合方法能够自适应地选择合适的奇异值个数进行信号重构,提高了降噪效果。 相似文献
5.
6.
针对验潮站水位变化序列非线性、非平稳特点,采用一种基于优化参数的变分模态分解和经验模态分解相结合的降噪方法。该方法先经过EMD分解原始信号后,得到低频和高频信号两个部分,再采用IVMD方法处理高频噪声部分,最后将两部分有效低频信号重构作为最终降噪信号。采用1组模拟数据和4个验潮站实测水位序列数据进行实验,并采用信噪比和均方根误差评价降噪效果,结果表明,EMD-IVMD方法明显优于EEMD和传统的EMD方法,该方法在信噪比精度指标上分别提升1.67%和1.52%,在均方根误差精度指标上分别提升9.59%和13.51%,验证该方法的有效性和可靠性。 相似文献
7.
针对能量密度谱判断噪声与信号主导模态分界点性能不稳定的问题,该文提出基于夹角余弦的分界点判断方法。该方法首先利用EEMD对CORS站高程数据进行分解,其次计算原始信号与各阶模态分量的夹角余弦值,最后根据夹角余弦值的首次逆向转折位置判定噪声主导模态和信号主导模态的分界点。以4个CORS基准站近20年原始高程时间序列信号为研究对象,对噪声与信号模态分量分界点进行判定,实验结果表明采用基于夹角余弦的判定方法可以一次判断出噪声和信号模态分量的分界点。该方法应用到CORS站高程信号与噪声序列的识别中性能更加稳定。 相似文献
8.
刘洋 《测绘与空间地理信息》2022,45(1):189-191,197
针对我国北斗系统变形监测数据中存在的噪声问题,本文利用自适应信号分析方法经验模态分解(EMD)对某北斗实测变形监测数据进行降噪处理。首先对E、N、U 3个方向的分量进行分解获取本征模态函数及趋势项,其次根据相关系数分离出噪声的本征模态函数,最后根据重构方法得到干净的位移序列。结果表明:EMD方法在北斗变形监测数据中的去噪是可行的,能有效分离信号与噪声,进一步提高了北斗观测的精度。 相似文献
9.
《武汉大学学报(信息科学版)》2020,(5)
为了提高变形监测数据的去噪精度及可靠性,基于变分模态分解(variational mode decomposition,VMD)构建一种新的变形监测数据去噪方法。首先,建立VMD高频噪声分量判定标准,引入T指标用于确定VMD去噪的最优K值。然后,将剔除高频噪声后的VMD分量进行叠加重构,建立VMD变形监测数据去噪方法。最后,通过仿真信号、桥梁、大坝变形监测数据去噪实例,对比分析VMD、小波及经验模态分解(empirical mode decomposition,EMD)去噪方法。实验结果表明,VMD对仿真信号去噪的相关系数、均方根误差、信噪比等指标均较大程度上优于小波及EMD去噪方法,理论上证实了VMD去噪方法的有效性及可靠性;VMD对桥梁、大坝变形监测数据去噪的结果比小波、EMD具有更好的精度及光滑性,同时较好地保留了局部变形特征信息。 相似文献
10.
为了准确提取桥梁GNSS监测数据中的有效变形特征,本文充分发挥自适应噪声完备集合经验模态分解(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, CEEMDAN)与小波变换(Wavelet Transform, WT)在信号降噪中的优势,将二者结合进行桥梁GNSS监测数据降噪。首先通过CEEMDAN方法将原始监测数据分解为若干个本征模态函数(Intrinsic Mode Function, IMF),并通过相关系数识别出有效IMF分量,包含噪声的IMF分量以及无效IMF分量;其次使用WT软阈值降噪方法对包含噪声的IMF分量进一步降噪;最后重构降噪后IMF分量与有效IMF分量。通过仿真实验数据与苏通大桥实测GNSS数据对本文方法的有效性与优越性进行检验,结果表明,本文方法具有良好的降噪效果,能够有效提取桥梁的真实变形信息。 相似文献
11.
针对时间序列混有的高频信息会影响地心运动规律分析的问题,采用网平移法对IGS提供的GNSS周解进行解算,得到2007-2017年地心运动时间序列,对其进行分解重构,剔除高频项,并利用重构时序对地心运动规律作进一步分析探讨。结果表明:本文解算的地心运动在Tx、Ty和Tz方向的精度均为毫米级。EMD方法重构的时序保留了原序列的基本信息,且抑制了高频项的影响,提高了周期贡献率,3个方向的贡献率分别提高了12.3%、16.7%及6.3%。通过分析重构后的时序发现,周年项振幅为各周期对应振幅的最大值,分别为2.32、1.89和2.07 mm;Ty和Tz方向长期变化趋势较Tx更为明显,分别为0.13和-0.27 mm/a;半年项较小,且在Tx和Ty方向上具有时变性。此外,还发现了一些其他较小的年际变化。 相似文献
12.
13.
14.
15.
地球质心(CM)是整个地球的质量中心,地心运动是由地球系统的质量重新分布激励的,特别是流体圈层.利用SLR对LAGEOS 1/2卫星的距离观测,解算1993-2006年期间的地心运动时间序列,然后分别利用小波变换和最小二乘法分析该序列,发现地心运动存在长期和周期性变化.地心的长期运动表明地壳形状在变化.季节性变化是地心运动的主项,主要是由地球流体圈层的质量分布造成的,如海洋、大气和陆地水等.地心运动还存在其他周期性和准周期性变化.地心运动存在2~5年的长周期变化.许多周期都存在渐变,这表明整个地球系统质量和环境存在不规则的变化.Abstract: The center of mass of Earth (CM) is the center of total Earth's mass. The geocenter motion may be excit-ed by the mass redistribution of Earth system, especially the fluid layer. A time series of geocenter motion meas-ured with SLR on LAGEOS 1/2 is estimated and then analyzed with the wavelet transformation and the least squaresmethod, respectively. The secular and periodic variations of geocenter motion are detected. The long-term move-ment indicates that the crustal figure is changing, the north hemisphere and 180-degree hemisphere are shrinking, and the south hemisphere and O-degree hemisphere are swelling. The seasonal variations are the main componentswhich may be caused from the mass distribution of Earth fluid layer, e.g. ocean, atmosphere and continental wa-ter, There are many other periodic or quasi-periodic variations. There are long periodic variations through 2 to 5 years, Many periods gradually change, which indicates that there exist nonregular fluxes for the environment and mass of the whole Earth system. 相似文献
16.
Estimating the motion of atmospheric water vapor using the Global Positioning System 总被引:1,自引:0,他引:1
Water vapor is both an important component in the atmosphere for the transport of energy and a noise source for space geodetic
observations of the Earth's surface, such as from GPS and interferometric SAR (InSAR) measurements. GPS data collected from
ground receivers are sensitive to the total amount of water vapor above the antenna and data from continuously operating GPS
receivers are routinely used to estimate delays caused by atmospheric water vapor. Using these time series of atmospheric
delay, we have estimated the motion of atmospheric water vapor above GPS networks. The motion above each site is determined
by comparing the time series from different sites and estimating relative time offsets in these time series. These are then
used to determine the velocity field of the atmospheric delays as they move across the network. We have compared the results
with similar estimates inferred from geostationary satellite data and found clear correlation on several occasions. Such results
can be useful for improving the understanding of the energy transport in the atmosphere, the spatial interpolation of water
vapor, and for calibrating InSAR observations for delays caused by water vapor.
Electronic Publication 相似文献
17.
P. H. Andersen 《Journal of Geodesy》1995,69(4):233-243
This analysis was performed with the GEOSAT software developed at NDRE for high-precision analysis of satellite tracking and VLBI data for geodetic and geodynamic applications. To determine the amplitudes of the tidally coherent daily and sub-daily variations in the Earth's orientation, geocenter, and crust, we have analyzed twelve months of SLR tracking data from the LAGEOS I & II and ETALON I & II satellites, obtained between October 1992 and September 1993. Station coordinates and mean geocenter are determined with an accuracy of 1 to 2 cm. Amplitudes of diurnal and semidiurnal variations in UT1, polar motion, and geocenter are determined with a precision of ~2µts, ~20µas, and 1–3 mm in each component. It is demonstrated that it is possible to determine a one-year continuous high-precision series in UT1 using multi-satellite laser ranging. 相似文献
18.
A novel approach to study vegetation dynamics is introduced, using the Empirical Mode Decomposition (EMD) to analyze NDVI time series. The NDVI time series which is nonlinear and nonstationary can be decomposed by EMD into components called intrinsic mode functions (IMFs), based on inherent temporal scales. The highest frequency component which has been found to represent noise is subtracted from the original NDVI series; thus smoothing the noisy signal. The different key features describing vegetation phenology have been extracted by analyzing the noise free signal. The lowest frequency component (last IMF) is the trend in the NDVI series. The trend in the series has been identified finding the Sen’s slope of last IMF, and the non-parametric seasonal Mann–Kendall test has been used to confirm the significance of the observed trend. The method has been applied on per–pixel basis to the SPOT Vegetation NDVI product covering Northeast India and surrounding regions for the time span of 1998–2009. Results show that the method has performed well in identifying the pixel clusters with significant trends. Hotspot regions with severe vegetation degeneration have been identified, and the relationship of the observed trends with the expected causative variables such as land use and land cover, topographic relief, and anthropogenic causes has been explored. The spatial locations of these critical regions closely matches with the findings of the previous studies carried out locally in the region, mainly indicating the shifting cultivation practice to be the main cause for land cover change. 相似文献