基于观测值域的GPS多路径误差应用分析

基于观测值域双差残差恒星日滤波方法消除多路径误差,利用事先计算的前一天观测卫星双差残差并进行去噪处理,根据各自卫星周日重复性改正到第二天对应时刻的双差观测方程中,得到剔除多路径误差后的"干净"双差观测值,最后解算基线信息。试验结果表明,与常规基于坐标域恒星日滤波相比,该方法在平面方向的精度可提高23%,且高程方向也有一定程度的提高。     

利用恒星日滤波改进精密单点电离层延迟提取

利用非组合精密单点定位(PPP)可以提取高精度的电离层延迟。测站多径误差会影响伪距和相位测量精度,影响实时PPP电离层延迟提取的精度以及收敛速度。对于静态观测站,利用对GPS卫星地面跟踪的时间重复性进行恒星日滤波可以消除多径误差的影响。通过事后处理提取前几日的码和载波相位残差序列,利用恒星日滤波建立多径误差改正模型,修正实时观测数据,可以改善实时电离层延迟估计性能。对IGS观测站的实测数据分析表明,应用恒星日滤波多径误差修正后,实时电离层延迟提取的精度由0.185m提高到0.028m,新进卫星的电离层参数估计收敛时间由80min减少为35min。     

球面多路径格网的恒星日滤波算法及其在PPP中的应用

针对传统的观测值域恒星日滤波在卫星精密单点定位（precise point positioning,PPP）应用中存在的问题,提出球面多路径格网的恒星日滤波算法并应用到静态PPP中,设计了确定格网大小的统计方法,给出了算法的实施步骤。利用全球分布的10个IGS监测站接收的GPS卫星实测数据进行了试验验证,结果表明,本文算法能够明显削弱地面连续运行监测站载波相位观测量的多路径误差;应用本文算法后,1~3 h的静态PPP定位精度得到明显提高,E、N、U三方向RMS分别提高41.59%、38.60%、36.96%。     

顾及分段相关性的恒星日滤波优化算法

多路径效应是高精度GPS动态变形监测中的主要误差来源之一。本文改进了常规恒星日滤波算法,提出了一种顾及分段相关性的恒星日滤波优化算法。本算法首先采用分段滑动窗口处理相邻两天的坐标残差时间序列,然后利用相关系数法求取各分段最佳相关延迟,对当天坐标残差序列进行恒星日滤波,得到削弱多路径效应后的坐标序列。研究结果表明,该算法较常规算法在N、E、U 3个方向上的滤波效果各提高7.7%、5.0%、11.2%,动态变形监测实验验证了本算法的有效性。     

顾及分段相似性的同震形变时间序列恒星日滤波优化算法

在常规恒星日滤波的基础上,提出一种顾及分段相似性的恒星日滤波优化算法。在构建滤波算子之前,首先确定用于构建滤波的坐标残差时间序列与地震当天坐标残差时间序列的分段相似性,然后根据相似性度量指标的大小确定权重后再进行滤波。研究结果表明:利用本文提出的顾及分段相似性的恒星日滤波优化方法能够显著改善单历元定位的精度和可靠性,克服常规滤波方法存在的缺陷,有利于恒星日滤波的无人工干预批处理。尤其是通用影像质量评价指数UIQI方法顾及了时间序列间的相关系数以及序列自身的均值和方差,比其他相似性度量更加科学合理,建议采用UIQI作为恒星日滤波优化算法的相似性度量。     

北斗GEO卫星载波相位单差观测值的周期性分析

高精度的卫星定位测量中,由于卫星在测站上空的重复周期性运动,周期性误差是影响测量精度的重要误差源。恒星日滤波利用周期性误差的特点,提取前一天的周期性误差来改正后一天的定位结果,可滤波处理诸如多路径误差。本文从北斗GEO卫星载波相位观测值站间单差的角度来探讨其周期性特点,通过前后两天站间相位单差观测值进行对比,采用相关性分析方法,验证北斗GEO卫星载波相位单差观测值的周期性特征。     

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.     

北斗系统星源伪距偏差特性分析及改正

北斗卫星伪距观测值存在一类与卫星相关的系统误差,称为星源伪距偏差,MEO和IGSO卫星可以通过其与高度角的关系建立改正模型,而GEO卫星由于其静止特性难以建立基于高度角的改正模型。据此,本文在分析GEO卫星伪距偏差特性的基础上,提出了一种基于奇异谱分析(SSA)的修正方法,并通过双频无电离层组合伪距单点定位(SPP)对比试验来验证修正效果。结果表明:修正后GEO卫星伪距观测值基本上消除了伪距偏差,MP观测值精度在B1、B2、B3频率上分别提高了39.9%、17.9%、29.4%,MW观测值精度提高了41.3%;传统改正模型修正IGSO和MEO卫星伪距偏差对SPP影响很小,而奇异谱分析方法修正GEO卫星伪距偏差使SPP的精度在平面、高程方向上分别提高了11.1%、21.1%。     

基于平方根信息滤波的北斗卫星实时定轨方法

连续、稳定、高精度的实时卫星轨道产品是北斗国际化、规模化、智能化应用的重要前提.当前，北斗卫星导航系统(BDS)的实时精密轨道产品多基于“批处理解算+轨道预报”的超快速模式获得，存在连续性较差、稳定性较低、精度不高等问题.为此，本文采用平方根信息滤波(SRIF)方法对北斗卫星精密轨道进行实时逐历元解算.实验结果表明：相比于超快速定轨模式，基于实时滤波方法的轨道产品能够有效避免边界跳变，具有更好的连续性和稳定性；同时，实时滤波定轨方法能够显著提高BDS的轨道精度，其中中轨道地球卫星(MEO)和倾斜地球同步轨道卫星(IGSO)的三维轨道误差分别减小了46%和68%，卫星激光测距(SLR)检核精度也普遍优于预报轨道.     

非差虚拟观测值的网络RTK实现

网络RTK是利用多基准站观测值实现流动站附近的误差建模,其中包括对流层、电离层延迟和卫星轨道等误差。讨论了利用VRS技术实现网络RTK中的几个关键技术,包括将多基准站插值所得的双差大气延迟添加到非差模式的VRS观测值中,利用IGS的超快速星历中的预报部分改正广播星历误差和利用恒星日滤波改正VRS的伪距多路径效应。24 ...     

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。     

小波分析和经验模态分解对BDS多路径误差削弱对比研究

在利用北斗卫星导航系统（BDS）进行高精度变形监测时,BDS信号产生的多路径效应是影响变形监测数据精度和可靠性的一个不可忽视的误差源. BDS有三种不同的轨道卫星,所形成的多路径误差较为复杂. 基于坐标域的多路径误差使用小波分析(Wavelet)和经验模态分解(EMD)进行原始序列降噪,对降噪后序列使用改进恒星日滤波(ASF)进行多路径误差剔除,两种方法分别对基线精度的E方向改善了38.6％和40.8％,N方向改善了59.1％和61.0％,U方向改善了57.8％和57.9％,EMD对坐标序列的平滑和基线精度改善较优.      

Enhancement of the accuracy of single-epoch GPS positioning for long baselines by local ionospheric modelling

Single-epoch relative GPS positioning has many advantages, especially for monitoring dynamic targets. In this technique, errors occurring in previous epochs cannot affect the position accuracy at the current epoch, but careful processing is required, and resolving carrier phase ambiguities is essential. Statistical ambiguity resolution functions have been used to determine the best values of these ambiguities. The function inputs include as a minimum the known base station position, the approximate roving antenna “seed” position, and the dual-frequency carrier phase measurements from both receivers. We investigate different solutions to find the ambiguity function inputs that achieve the highest ambiguity resolution success rate. First, we address the rover seed position. A regionally filtered undifferenced pseudorange coordinate solution proves better than a double-differenced one. Multipath errors approximately repeat themselves every sidereal day in the case of static or quasi-static antennas; applying a sidereal filter to the pseudorange-derived positions mitigates their effects. Second, we address the relative carrier phase measurements, which for medium to long baselines are significantly affected by ionospheric propagation errors imperfectly removed during differencing. In addition to the International GNSS Service ionospheric model, we generate a local pseudorange-based ionospheric correction. Applying this correction improves the quality of the phase measurements, leading to more successful ambiguity resolution. Temporally smoothing the correction by means of a Kalman filter further improves the phase measurements. For baselines in the range 60–120 km, the mean absolute deviation of single-epoch coordinates improves to 10–20 cm, from 30–50 cm in the default case.     

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.     

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.     

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。     

削弱GNSS多路径效应的半天球格网点建模方法

多路径效应与测站观测环境相关,无法利用差分的方式消除或削弱,是GPS精密数据处理时的主要误差源。为此,本文提出了一种利用半天球格网点模型削弱测站多路径误差影响的新方法MHGM(multi-point hemispherical grid model)。该方法对以测站为球心的半天球进行格网点划分,通过格网点的参数化描述,实现测站处多路径误差建模,可适用于现有各类GPS数据处理的软硬件设备和观测环境。本文算例的测试结果表明,采用新方法建模后双差观测值残差的RMS均值改善率平均在73.9%左右,较传统恒星日滤波方法的改善效果平均可提升26.9%,对于静态观测数据,按实时动态相对定位处理模式可获得平面精度约1.7 mm,高程精度约3.0 mm的定位结果。此外,MHGM模型还可进一步用于对测站周边可能存在的多路径误差源进行方位评估,对从物理意义上消除多路径误差源的影响给出一定的指导性意见。     

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.     

Statistical modeling for the mitigation of GPS multipath delays from day-to-day range measurements

Based on a least-squares model for double-difference GPS pseudoranges and carrier-phases, measurement residuals expressed in time series during an observation session are positively correlated between one sidereal day and the preceding days. As a result of the satellite’s period, the phenomenon, which takes place at a user receiving site, is attributed to multipath interference. Examples from a weekly measurement dataset of control baselines are shown, where the known end-point coordinates also serve as a benchmark for assessing positioning accuracy. The system of error equations for mixed-model adjustment is divided into two subsystems. One set of the error equations is related to the real range measurements, while the other involves the pseudo-observation with an empirical sample variance. According to the existing correlation between day-to-day residual estimates, a multipath-mitigating algorithm is proven to improve the accuracy of the GPS height determination by at least 40%. It is also found that the algorithm depends on a variance-component estimator that adaptively scales an error covariance matrix for both the real range and empirical delay measurements.