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.     

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.     

基于自适应噪声抵消与小波滤波的GPS监测误差分析

对GPS载波相位测量误差进行了理论分析和试验研究。根据噪声特征以及它们对载波相位测量结果的影响，提出了基于自适应噪声抵消与小波滤波相结合的GPS噪声抑止方法。对具有强相关特性的多路径误差采用自适应噪声抵消方法，而对于不相关的高频噪声则通过合理选择小波分解层数对信号进行分解，对作用闽值后的小波系数进行重构，得到最后的变形信号。实测数据分析表明，该方法能有效地削弱多路径效应及测量随机噪声，较用单一方法对GPS数据进行消噪处理有较大的优越性。     

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。     

GPS多路径重复性试验研究

信号衍射和多路径效应是限制GPS定位精度进一步提高的瓶颈,观测中两者往往同时存在。衍射效应具有不完全重复的特性,为了研究多路径重复性并使其不受信号衍射的影响,本文提出先应用SIGMA-Δ信噪比加权模型削弱信号衍射的影响,再运用改进恒星时滤波法分析多路径重复性。实验观测资料分析表明,SIGMA-Δ模型能有效削弱衍射信号对定位结果影响,并且保留主要的多路径信号;而改进恒星时滤波法能适应测站周围多路径环境的变化,从而最大限度地削减多路径误差。两者组合比传统的恒星时滤波更能有效提高GPS的动态定位精度。     

基于小波分析的GPS双差观测数据消噪方法研究

通过双差方法难以消除的多路径误差、接收机观测噪声等非模型化误差会大大影响GPS动态定位的精度.基于小波分析的理论,对GPS双差观测数据序列的消噪问题进行研究,结果表明,利用小波去噪原理,可有效地从受到强噪声干扰的GPS观测数据序列中提取变形特征信息,增强基线解算的有效性,提高GPS定位精度,解决传统技术对GPS动态监测...     

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。     

基于小波变换的GPS载波相位测量误差分析

多路径效应、接收机电热噪声等个性观测因素是现代GPS双频载波相位差分技术仍然无法克服的GPS测量误差源。利用小波变换，通过多尺度分解筛选的方法，可以将不同误差源的影响从GPS测量结果中有效分离出来，进而达到提高GPS定位精度的目的。     

GPS残差信号提取的时间序列分析方法

作者使用零基线单差模型研究并讨论了GPS量测噪声的时相关特性,并得出了GPS观测噪声具有类似白噪声或一阶高斯马尔科夫噪声的特殊性质。利用观测噪声所具备的这种特殊性质,作者首先尝试了从时间域的角度分离和提取了残差序列中的多路径信号特性,成功验证了多路径信号在连续两个恒星日内会有所重复的结论。作者提出了一种使用时相关分析法检测观测序列中周期信号(如冰期反弹、固体潮、极潮等)的设想并就其可行性进行了分析研究。最后,针对观测噪声的这种特殊性质,探讨了结合使用小波分析法提取GPS沉降监测信号的可行性。     

GPS多路径效应实验测试与分析研究

作为影响GPS测量信号的干扰源,多路径效应为众多研究者所关注。多路径效应使得GPS测量的精度降低,严重时还将引起信号失锁。针对这一问题,文中从多路径效应产生的原理出发,设计并实施无线电干扰和镜面反射干扰实验,分析多路径效应对GPS单点定位精度、基线长度精度和网精度的影响。在既有的研究成果基础上,提出随着距离的变化多路径效应的影响程度不同、对网精度影响最小、对单点定位精度影响最大等结论,并总结了减弱多路径效应的相关措施。     

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.     

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.     

Multipath Mitigation of Continuous GPS Measurements Using an Adaptive Filter

Though state-of-the-art dual-frequency receivers are employed in the continuous Global Positioning System (CGPS) arrays, the CGPS coordinate time series are typically very noisy due to the effects of atmospheric biases, multipath, receiver noise, and so on, with multipath generally being considered the major noise contributor. An adaptive finite-duration impulse response filter, based on a least-mean-square algorithm, has been developed to derive a relatively noise-free time series from the CGPS results. Furthermore, this algorithm is suitable for real-time applications. Numerical simulation studies indicate that the adaptive filters is a powerful signal decomposer, which can significantly mitigate multipath effects. By applying the filter to both pseudorange and carrier phase multipath sequences derived from some experimental GPS data, multipath models have been reliably derived. It is found that the best multipath mitigation strategy is forward filtering using data on two adjacent days, which reduces the standard deviations of the pseudorange multipath time series to about one fourth its magnitude before correction and to about half in the case of carrier phase. The filter has been successfully applied to the pseudorange multipath sequences derived from CGPS data. The benefit of this techniques is that the affected observable sequences can be corrected, and then these corrected observables can be used to improve the quality of the GPS coordinate results. ? 2000 John Wiley & Sons, Inc.     

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.     

BDS-3新频点B1C/B2a动态数据特性及PPP精度分析

通过载噪比(CNR)、数据完整率、伪距与载波相位观测值噪声和伪距多路径效应四个指标对北斗三号卫星导航系统(BDS-3)新频点B1C/B2a车载动态数据的特性进行了分析,测试了BDS-3新频点动态精密单点定位(PPP)的性能,并与其它全球卫星导航系统(GNSS)进行了对比. 试验结果表明,BDS-3新频点B2a平均CNR优于北斗卫星导航系统(BDS)其它频率,但略差于GPS L5;相较于其它GNSS,BDS数据完整率相对较高,其中BDS-3 B2a新频点数据完整率最高;BDS-3 B2b伪距观测值噪声最小,B1C和B2a伪距观测值噪声约为B2b信号的3倍,但不同频率相位观测值噪声处于同一量级;对于伪距多路径而言,BDS-3 B1C/B2a 信号略小于B2b 信号. 总体而言,GPS L5信号抑制多路径效应的能力最强. 在动态PPP性能方面,BDS-3 B1C/B2a双频组合动态PPP定位精度最优,其三维(3D)均方根(RMS)误差为0.439 m,相比BDS B1I/B3I、GPS L1/L2、GLONASS G1/G2和Galileo E1/E5a双频组合PPP,其精度改善率分别为49%、56%、81%和42%.