一种基于交叉证认技术的自适应小波变换及其在削减GPS多路径误差中的应用

多路径效应影响是目前限制GPS定位精度进一步提高的瓶颈。提出用交叉证认技术自动识别小波分解的信号层,再通过小波重构实现降噪和信号提取,并将该方法应用于GPS多路径误差的削减中。对模拟数据和实际GPS观测资料的分析表明,该方法能合理分离不同噪声水平下资料序列中的信号和噪声;当噪声水平小于信号振幅的一半时,能成功分离资料序列中的高频信号。同时,运用该方法得到的多路径改正模型和GPS多路径效应的重复性,可有效地削弱多路径效应的影响,提高GPS定位精度。     

GPS多路径误差滤波方法比较研究

基于GPS多路径时间序列,分别采用Vondrak滤波、经验模式分解(EMD)和小波滤波3种方法构建了GPS多路径误差修正模型,并将其用于削弱多路径效应.通过对模拟数据及实测数据的分析表明,3种方法都能有效地分离不同噪声水平下时间序列中的信号和噪声.同时,利用3种方法构建的多路径修正模型可有效地削弱多路径效应的影响,提高GPS定位精度,但3种方法的优缺点各有不同.     

基于交叉证认的EMD滤波及其在GPS多路径效应中的应用

利用交叉证认的方法,提出一种新的基于EMD滤波去噪方法,并将其应用于GPS多路径效应的研究中。通过模拟实验及实测数据分析表明,该方法能够自适应地选择IMF中的信号层数,削弱随机噪声,合理地分离信号和噪声。利用该滤波方法去噪并建立具有重复性的多路径误差效应改正模型,可有效地削弱多路径效应的影响,进而提高GPS动态变形监测的精度。     

CVVF方法用于GPS多路径效应的研究

将交叉证认法与Vondrak数字滤波器相组合,提出一种分离测量资料中信号与噪声的新方法,即CVVF方法,并将该方法用于GPS多路径效应的研究中.对数字模拟试验和实际观测资料的分析表明,该方法能最大限度地削弱测量的随机误差,使资料序列中的信号和噪声合理分离.同时,利用GPS多路径效应周期性重复的特性,可有效地削弱多路径效应对观测结果的影响,从而提高GPS定位精度.     

奇异谱分析滤波法在消除GPS多路径中的应用

基于奇异谱分析(singularspectrumanalysis,SSA)的基本思想,利用噪声与信号的赫斯特(Hurst)指数有显著差异这一特性,提出了一种新的SSA滤波法,同时给定了嵌入维数犔与重构阶次犘的确定标准,并将该方法应用于GPS多路径的研究中。通过模拟数据及实测GPS坐标序列的数据分析,结果表明SSA滤波法是一种有效的去噪方法,其去噪效果与小波滤波与经验模态分解(empriricalmodedecomposition,EMD)滤波相当。针对多路径效应周日重复性的特点,利用该滤波方法建立改正模型,可有效地削弱多路径效应的影响,进而提高GPS动态变形监测的精度。     

GPS动态监测中多路径效应的规律性研究

在分析GPS多路径效应特性的基础上,结合GPS动态变形监测的特点,对连续多天的观测数据解算结果采用滤波与周日位置求差相结合的方法进行数据处理。研究结果表明,多路径误差具有较强的周日重复性,在有效地削弱多路径效应的影响后,GPS动态监测的三维位置精度可以达到mm级,尤其是垂直方向的精度改善明显。     

基于观测值质量指标的GPS观测量随机模型分析

卫星高度角、载噪比及信号强度是反映GPS观测值质量的重要指标,基于这些指标的随机模型可进一步削弱GPS残余大气延迟、衍射以及多路径效应等误差的影响,但这些随机模型对不同误差的处理效果具有一定差异.为合理利用这些随机模型,利用实际观测数据进行了比较计算,结果表明信号强度和载噪比随机模型的效果基本一致,当观测数据中载噪比输出时,可用信号强度代替载噪比建立随机模型;信号强度和载噪比随机模型对削弱衍射误差十分有效,而高度角随机模型能更有效地削弱残余对流层延迟误差.     

基于小波变换的GPS动态变形分析

介绍了小波滤波去噪的原理并讨论在动态环境下GPS多路径效应的重复性问题。首先利用小波方法对静态观测数据的残差进行去噪,提取具有系统性的多路径误差改正模型。然后,根据多路径误差重复性的特点,对后续动态观测的数据进行改正。通过实验证明:经重复性模型改正后残余的多路径噪声符合正态分布的特点。然后采用小波软阈值去噪方法,消除剩余的多路径误差,可进一步提高动态变形监测的精度。     

非差改正数的多GNSS恒星日滤波方法

多路径误差是全球卫星导航系统(GNSS)精密数据处理的主要误差源之一,观测值域的恒星日滤波是应用较为广泛的多路径误差削弱方法,但其在多系统应用中存在映射方法相对低效的问题,且缺乏针对Galileo的应用研究.为解决现有方法不适用于大规模网解数据处理的弊端,该文提出了一种基于非差改正数的多GNSS系统恒星日滤波方法,从理论上证明了该方法在进行多路径误差改正时的效果与双差观测值域恒星日滤波方法的等价性,并提出了该方法在Galileo中的应用服务方案.对一组采集于2018年年积日219-231 d的多系统数据进行了多路径误差改正实验分析,观测值域的双差残差测试结果表明,GPS、BDS和Galileo的双差残差统计精度得到了显著的提升,频域分析结果也表明多路径误差的低频部分能被有效削弱.坐标域的静态定位结果显示,强多路径环境下GPS和BDS联合定位精度在三维方向上相对于单GPS有一定改善,加入Galileo数据后相对于单GPS可进一步提升.     

基于经验模式分解的滤波去噪法及其在GPS多路径效应中的应用

经验模式分解(Empirical Mode Decomposition,EMD)是一种新的信号处理技术,它是基于数据本身的,且能在空间域中将信号进行分解,从而可以区分噪声和有用信号。根据EMD分解白噪声而得到的本征模式函数(IMF)分量的能量密度与其平均周期的乘积为一常量这一特性,建立一种新的基于EMD滤波去噪方法,并将该方法应用于GPS多路径效应的研究中。通过对模拟数据与GPS实测数据的处理分析,得出以下主要结论:①EMD滤波去噪法与小波方法都能最大限度地削弱测量的随机误差,但EMD滤波去噪法比小波方法更直接,且不受测不准原理及小波函数选择的影响;②相比小波方法,EMD能够更有效地剔除瞬时强噪声,从而能够提取更精确的多路径效应重复性误差改正模型。     

Adaptive wavelet transform based on cross-validation method and its application to GPS multipath mitigation

Global positioning system (GPS) multipath disturbance is a bottleneck problem that limits the accuracy of precise GPS positioning applications. A method based on the technique of cross-validation for automatically identifying wavelet signal layers is developed and used for separating noise from signals in data series, and applied to mitigate GPS multipath effects. Experiments with both simulated data series and real GPS observations show that the method is a powerful signal decomposer, which can successfully separate noise from signals as long as the noise level is lower than about half of the magnitude of the signals. A multipath correction model is derived based on the proposed method and the sidereal day-to-day repeating property of GPS multipath signals to remove multipath effects on GPS observations and to improve the quality of the GPS measurements.     

Filtering GPS time-series using a Vondrak filter and cross-validation

Multipath disturbance is one of the most important error sources in high-accuracy global positioning system (GPS) positioning and navigation. A new data filtering method, based on the Vondrak filter and the technique of cross-validation, is developed for separating signals from noise in data series, and applied to mitigate GPS multipath effects in applications such as deformation monitoring. Both simulated data series and real GPS observations are used to test the proposed method. It is shown that the method can be used to successfully separate signals from noise at different noise levels, and for varying signal frequencies as long as the noise level is lower than the magnitude of the signals. A multipath model can be derived, based on the current-day GPS observations, with the proposed method and used to remove multipath errors in subsequent days of GPS observations when taking advantage of the sidereal day-to-day repeating characteristics of GPS multipath signals. Tests have shown that the reduction in the root mean square (RMS) values of the GPS errors ranges from 20% to 40% when the method is applied.     

Sidereal filtering based on single differences for mitigating GPS multipath effects on short baselines

Carrier-phase multipath effects are one of the most significant error sources in precise Global Positioning System (GPS) positioning applications. A new sidereal filtering algorithm based on single differences is developed to mitigate multipath effects for short-baseline high-rate GPS applications such as structural deformation monitoring. This method differs from traditional sidereal filtering in that our method operates on the single differences rather than the coordinates or double differences. A multipath model for the single differences on the reference day is established for each satellite and is used to remove multipath errors from observations of subsequent days by taking advantage of the sidereal repeatability of multipath signals. Using both simulated and real GPS observations, we demonstrate that this method is insensitive to different weighting strategies used in computing single differences from double differences. Applying the proposed method can reduce the root mean square (RMS) of positioning noises by 82% on average. Compared to sidereal filtering (in either coordinate or double differences domain) and aspect repeat time adjustment, this method can further reduce the RMS values by 13 and 7%, respectively. Wavelet spectra have shown that the proposed method is more effective in mitigating multipath errors of both long and short periods. This method is also more advantageous in that it is applicable when different GPS satellites are observed on different days.     

Multipath mitigation via component analysis methods for GPS dynamic deformation monitoring

Multipath is one of the main error sources in high-precision global positioning system (GPS) dynamic deformation monitoring, as it is difficult to be mitigated by differencing between observations. In addition, since a specific frequency threshold value between multipath and deformation signals may not exist, multipath is usually inseparable from the low-frequency vibration signal using conventional frequency-domain filter methods. However, the multipath repeats in two sidereal days when the surroundings of a GPS antenna remain unchanged. This characteristic can be exploited to model and thus mitigate multipath effectively in dynamic deformation monitoring. Unfortunately, a major issue is that the degree of repeatability decreases as the interval between first day and subsequent days increases. To overcome this problem, we develop a new sidereal filtering referred to as reference EMD-ICA (EMD-ICA-R), where empirical mode decomposition (EMD) and independent component analysis (ICA) are jointly used to model multipath and renew the reference multipath. For the successful implementation of the EMD-ICA-R, an a priori denoised multipath signal is needed as a reference. We further propose to use the principal component analysis (PCA) method to extract more accurate reference multipath signal and form a combined PCA-EMD-ICA-R approach. Simulation experiments with a motion simulation platform were conducted, and the testing results indicate that the proposed methods can mitigate the multipath by around 67 % when a reliable reference multipath signal is extracted from a static situation. Furthermore, simulation experiments with different deformation signals added into the coordinate time series of three consecutive days show that the two proposed methods are also effective in a dynamic situation. Since wavelet filtering is used to denoise the reference multipath signals in the new approaches, simulation experiments with several wavelet filters are tested, and the results indicate that the PCA-EMD-ICA-R approach can work well with various wavelet filters.     

Characteristics of multipath effects in GPS dynamic deformation monitoring

IntroductionThere are many factors that affect the precisionand reliability of GPS measuring. Generally, theerror sources can be grouped into the followingthree classes: errors correlated with the satellites(such as ephemeris error, satellite clock error,…     

GPS码跟踪环自适应多径消除算法

根据多径信号的产生机理,在对GPS接收机中的码跟踪环多径信号模型研究的基础上,提出了采用自适应滤波的来消除GPS多径效应的算法。自适应滤波的方法不需要估计模型的系统参数,而直接通过自适应滤波将多径信号滤除。在有噪声的情况下,自适应滤波的RLS算法是最小二乘意义下的最优估计,仿真的结果表明采用自适应滤波算法可以快速的消除多径的影响,修正鉴相函数的过零点偏差,提高码跟踪环的跟踪精度。由于自适应滤波算法是递推算法,易于软、硬件实现。