不同太阳活动条件下电离层形态对估算GPS系统硬件延迟的影响

利用两个中纬度台站GPS观测数据提取的GPS卫星硬件延迟,分析了不同太阳活动情况下估算的硬件延迟稳定性和统计特征,结合同期电离层观测数据,研究了电离层状态对硬件延迟估算结果的影响.研究结果表明,基于太阳活动高年(2001年)GPS观测数据估算的硬件延迟稳定性要低于太阳活动低年GPS观测数据的估算结果,利用2001年GPS数据估算的卫星硬件延迟年标准偏差(RMS)平均值约为1TECU,而2009年GPS数据估算的卫星硬件延迟年标准偏差平均值约为0.8TECU.通过对2001年和2009年北京地区电离层F₂层最大电子密度(N_mF₂)变化性分析,结合GPS硬件延迟估算方法对电离层时空变化条件的要求,认为硬件延迟稳定性与太阳活动强度的联系是由不同太阳活动条件下电离层变化的强度差异引起的.      

利用北斗观测数据实时监测中国区域电离层变化

北斗卫星导航信号采用三个频点工作,可以利用伪距双频组差方法解算电离层电子含量,为实时监视中国区域电离层变化提供新的技术手段.中国中低纬度处于电离层赤道异常变化区,在北纬20°±5°区域时常发生较大梯度的电离层变化.利用北斗实时多频伪距和相位观测数据,采用相位平滑伪距方法计算电离层穿刺点电子含量,分析通过北斗系统GEO卫星监测的电离层周日变化特性;采用多面函数方法拟合中国区域1°×1°分辨率的电离层延迟量,每5min绘制一幅中国区域电离层图,观测区域所有电离层穿刺点拟合残差RMS为2.778TECU;分析北斗系统实时监测中国区域电离层异常情况,当发生电离层异常变化时,相邻两天的VTEC（Vertical Total Electronic Content）峰值相差约60TECU.      

Kalman滤波估算电离层延迟的一种优化方法

频间偏差（Inter Frequency Bias,IFB）通常会给电离层延迟的解算带来误差.目前从电离层延迟中消除频间偏差的方法是基于GPS双频观测数据建立垂直电离层模型,利用卡尔曼滤波实时估算电离层模型系数和频间偏差.然而滤波过程中的测量噪声协方差矩阵没有考虑系统观测量之间的相关性,这会导致滤波模型不准确,进而影响最后求解的电离层延迟的准确性.本文选取了美国19个参考站的GPS双频观测数据,利用卡尔曼滤波实时估算电离层模型系数以及频间偏差.在滤波过程中,通过将先验频间偏差的估计方差引入测量噪声方差,实现对测量噪声协方差矩阵的优化.计算结果表明,优化后得到的卫星频间偏差与欧洲定轨中心（Center for Orbit Determination in Europe,CODE）得到的频间偏差更接近.将优化后的电离层延迟代入伪距解算,得到的位置误差的标准差在东向和天顶向分别下降了12.5%和15.4%,天顶向误差平均值下降了17.6%,定位精度得到提高.      

基于双频GPS观测的电离层TEC与硬件延迟反演方法

利用全球定位系统（Global Positioning System,GPS）的双频观测数据反演得到电离层的总电子含量（Total Electron Content,TEC）,使得广域甚至全球范围高时空分辨率的电离层观测研究成为可能,但由于GPS卫星和接收机对信号的硬件延迟可导致TEC测量系统偏差,因此,需要探索反演TEC并估测GPS卫星与接收机硬件延迟的有效算法.本文根据电离层电波传播理论,阐述了基于双频GPS观测提取电离层TEC的方法,给出TEC与硬件延迟的基本关系.综合研究了TEC与硬件延迟的反演方法,进行分析与归纳分类,在此基础上提出了有待深入研究的问题.      

电离层周日变化对解算GPS硬件延迟稳定性的影响

针对电离层周日变化特征分析了其可能对SCORE方法估算的硬件延迟稳定性的影响. 利用BJFS以及XIAM台站的GPS观测数据, 解算了位于太阳活动高年(2001年)和太阳活动低年(2009年)的卫星硬件延迟并分析了估算的硬件延迟的稳定性. 研究发现, 电离层周日变化对估算的硬件延迟稳定性具有一定影响, 但是利用不同台站所得到的卫星硬件延迟稳定性在昼夜不同时间上的解算结果存在一定差异. 电离层周日变化对利用 BJFS台站数据解算的硬件延迟稳定性日夜差异较为明显, 在太阳活动高年利用XIAM 台站数据解算的硬件延迟日夜稳定性差异不很明显, 由于XIAM台站处于电离层赤道异常峰附近, 夜间电离层变化很大, 因此对比中纬度地区, 电离层周日变化对赤道异常峰附近地区硬件延迟稳定性解算结果的影响相对较小, 但在太阳活动低年, 其影响仍较为显著.      

基于北斗GEO卫星双频观测值连续监测电离层延迟

利用北斗GEO卫星观测数据直接计算电离层延迟。由于GEO卫星具有固定穿刺点和静地的特性,使得观测站监测电离层变化时可不考虑空间变化,并可进行连续不间断监测。通过分析北斗GEO卫星三种频率码伪距和载波相位观测值不同组合,选取B1&B2双频计算电离层延迟为最优组合,采用相位平滑伪距的方法计算电离层延迟TEC,相较其他电离层模型,该方法的优点是不会引入模型误差,可得到连续的高精度的电离层延迟监测结果。     

南极GPS定位中的电离层延迟分析

基于南极地区国际GNSS服务组织(IGS,International GNSS Service)跟踪站的全球定位系统(GPS,Global Position System)双频实测数据,分析了南极地区电离层延迟的变化情况及其二阶项延迟对南极GPS定位结果的影响.结果表明:南极地区的总电子含量(TEC,Total Electron Content)日间波动频繁,其日间TEC最大值变化较中纬度地区剧烈;在南极地区夏季,电离层二阶项延迟对GPS定位结果的影响可达cm级.同时,由于欧洲定轨中心(CODE,The European Center for Orbit Determination)提供的全球电离层模型(GIM,Global Ionosphere Maps)在南极区域应用的局限性,通过选取南极地区6个IGS跟踪站作为基准站建立了区域电离层TEC模型(RIM,Regional Ionosphere Model).经实测数据计算证明,对于南极地区,RIM的定位精度在一定程度上优于全球电离层模型GIM.     

单接收机GNSS数据组合硬件延迟的联合求解方法

利用GNSS观测数据解算TEC的最大误差源是硬件延迟,包括卫星硬件延迟和接收机硬件延迟.在单接收机情况下,由于数据稀疏以及接收到的卫星信号时间不对齐等特点,已有的解算硬件延迟方法的求解结果往往不理想.在应用局域模式拟合方法和SCORE方法求解单接收机数据基础上,利用局域模型拟合法在电离层平静期拟合较准确的优点,提出一种联合改进方法,同时改正了SCORE方法解算过程中约束过强的缺点.通过利用GPStation-6接收机的GPS和BDS实际观测数据进行解算分析,验证了所提方法的有效性与准确性.      

Klobuchar模型的实用分析与改进

电离层延迟误差是无线电信号传播中不可忽视的误差源.GPS特许用户利用双频接收机的双频观测值直接对电离层延迟进行实时测定,其所得结果精度很高.多数普通用户所使用的单频接收机依靠电离层模型对其进行误差修正,效果不很理想.本文通过WUHN观测站双频接收机10天的实测数据对GPS广播星历采用的Klobuchar模型进行了验证,其结果与前人论述相一致.此外,经由反映太阳活动强度的太阳相对黑子数对Klobuchar模型提出了新的改进方法.实验数据结果表明,该方法对此模型修正效果有大幅提升,对原模型修正效果>50%的修正率由60%左右提升至85%以上,>80%的修正率由10%左右提升至40%以上.在实际应用中单频接收机用户可以参考本文改进方法对Klobuchar模型进行修正.      

多GNSS全球电离层建模特性及其精度检验

分别以GPS单系统和融合BDS,GPS,GLONASS三系统两种方案,采用载波相位平滑伪距观测值和球谐函数,构建了全球电离层延迟模型并进行了对比和分析.本文以GPS单系统和融合三系统两种方法反演了2014年1月每日电离层变化过程,解算得出了频间偏差的月综合产品,并对结果进行了对比和分析.事实上,三系统融合不仅增加了可观测的卫星数,而且改善了穿刺点的几何分布.分析结果表明,三系统融合反演全球电离层在精度上优于GPS单系统,均有5~10 TECU的提高.计算得到的频间偏差结果显示,GPS优于GIONASS,BDS稳定性则较次之.     

赤道异常区GPS-TEC与系统硬件偏差的反演

提出了一种基于最小二乘的赤道异常区GPS-TEC与系统硬件偏差的反演方法.利用设置在福州、厦门、广州和南宁4个台站的观测数据,可以得到GPS卫星和接收机的硬件偏差以及(20°～28°N,105°～123°E)区域中48个3°×1°网格的TEC(时间分辨率为15 min).应用于2006年观测数据,得到了较稳定的系统硬件偏差,其中卫星硬件偏差值与欧洲定轨中心公布的结果接近,得到的TEC具有合理的日变化和季节变化特征.该反演方法可以应用于赤道异常区电离层的研究.      

电离层多普勒接收机的设计与实验

利用软件无线电技术设计了一种电离层多普勒接收机. 该接收机采用DSP, FPGA等数字芯片与PXI总线进行架构, 使用GPS作为接收机的时间和频率同 步模块, 能够灵活设置系统参数. 实验接收来自中国蒲城陕西天文台的高 频时间信号, 实时获取由于电离层扰动所产生的多普勒频率偏移信息. 使用通过MATLAB语言实现的信号处理软件平台, 对接收到的高频信号进行处理. 观测结果表明, 接收机能够分析电离层回波信号的多普勒频移随时间的变化, 是获取不同空间尺度电离层扰动信息的一种有效手段.      

GPS卫星信标信噪比的统计分析

GPS卫星信标信噪比(SNR)受到卫星上的发射机增益、地面站的接收机增益、卫星与接收机之间的几何距离、接收机处的仰角、信标传播路径上电离层介质衰减等因素的共同影响.首先提出了一种从SNR观测数据中分离出SNR在电离层中衰减的算法,并应用于MANA接收站(273.751°E,12.149°N)2003年的观测数据,得到了一个电离层介质引起的对L波段信标SNR衰减日变化的平均情况.通过与IRI模式计算的电波垂直穿过电离层时衰减的SNR比较发现,由我们得到的SNR在电波垂直穿过电离层时的衰减与IRI模式计算的衰减随地方时的变化符合得比较好.      

NeQuick G model based scale factor determination for using SBAS ionosphere corrections at low earth orbit

A space-based augmentation system (SBAS) provides real-time correction data for global navigation satellite system (GNSS) users near ground. In order to use the SBAS ionosphere correction for low Earth orbit (LEO) satellites, the correction should be scaled down for the LEO altitude. This scale factor varies with ionosphere distribution and it is hard to determine the value at LEO in real time. We propose a real-time scale factor determination method by using Galileo GNSS’s NeQuick G model. A LEO satellite GPS data and SBAS data received on ground were used to evaluate the performance of the NeQuick G derived variable scale factor. The NeQuick G derived scale factor shows a significant accuracy improvement over NeQuick G model or pre-determined constant scale factor. It improves a vertical positioning accuracy of the LEO satellite. The error mean reductions of the vertical positioning over NeQuick G and the constant scale factor are 31.5% and 11.7%, respectively.     

Near real-time PPP-based monitoring of the ionosphere using dual-frequency GPS/BDS/Galileo data

Ionosphere delay is very important to GNSS observations, since it is one of the main error sources which have to be mitigated even eliminated in order to determine reliable and precise positions. The ionosphere is a dispersive medium to radio signal, so the value of the group delay or phase advance of GNSS radio signal depends on the signal frequency. Ground-based GNSS stations have been used for ionosphere monitoring and modeling for a long time. In this paper we will introduce a novel approach suitable for single-receiver operation based on the precise point positioning (PPP) technique. One of the main characteristic is that only carrier-phase observations are used to avoid particular effects of pseudorange observations. The technique consists of introducing ionosphere ambiguity parameters obtained from PPP filter into the geometry-free combination of observations to estimate ionospheric delays. Observational data from stations that are capable of tracking the GPS/BDS/GALILEO from the International GNSS Service (IGS) Multi-GNSS Experiments (MGEX) network are processed. For the purpose of performance validation, ionospheric delays series derived from the novel approach are compared with the global ionospheric map (GIM) from Ionospheric Associate Analysis Centers (IAACs). The results are encouraging and offer potential solutions to the near real-time ionosphere monitoring.     

Rapid local ionosphere modeling based on Precise Point Positioning over Iran: A case study under 2014 solar maximum

Presently, the ionosphere effect is the main source of the error in the Global Positioning System (GPS) observations. This effect can largely be removed by using the two-frequency measurements, while to obtain the reasonable results in the single-frequency applications, an accurate ionosphere model is required. Since the global ionosphere models do not meet our needs everywhere, the local ionosphere models are developed. In this paper, a rapid local ionosphere model over Iran is presented. For this purpose, the GPS observations obtained from 40 GPS stations of the Iranian Permanent GPS Network (IPGN) and 16 other GPS stations around Iran have been used. The observations have been selected under 2014 solar maximum, from the days 058, 107, 188 and 271 of the year 2014 with different geomagnetic activities. Moreover, ionospheric observables based on the precise point positioning (PPP) have been applied to model the ionosphere. To represent our ionosphere model, the B-spline basis functions have been employed and the variance component estimation (VCE) method has been used to regularize the problem.To show the efficiency our PPP-derived local ionosphere model with respect to the International GNSS Service (IGS) global models, these models are applied on the single point positioning using single-frequency observations and their results are compared with the precise coordinates obtained from the double-differenced solution using dual-frequency observations. The results show that the 95th percentile of horizontal and vertical positioning errors of the single-frequency point positioning are about 3.1 and 13.6?m, respectively, when any ionosphere model are not applied. These values significantly improve when the ionosphere models are applied in the solutions. Applying CODE’s Rapid Global ionosphere map (CORG), improvements of 59% and 81% in horizontal and vertical components are observed. These values for the IGS Global ionosphere map (IGSG) are 70% and 82%, respectively. The best results are obtained from our local ionosphere model, where 84% and 87% improvements in horizontal and vertical components are observed. These results confirm the efficiency of our local ionosphere model over Iran with respect to the global models. As a by-product, the Differential Code Biases (DCBs) of the receivers are also estimated. In this line, we found that the intra-day variations of the receiver DCBs could be significant. Therefore, these variations must be taken into account for the precise ionosphere modeling.     

A MARS-based method for estimating regional 2-D ionospheric VTEC and receiver differential code bias

The geometry-free linear combination of dual-frequency GNSS reference station ground observations are currently used to build the Vertical Total Electron Content (VTEC) model of the ionosphere. As it is known, besides ionospheric delays, there are differential code bias (DCB) of satellite (SDCB) and receiver (RDCB) in the geometry-free observation equation. The SDCB can be obtained using the International GNSS Service (IGS) analysis centers, but the RDCB for regional and local network receivers are not provided. Therefore, estimating the RDCB and VTEC model accurately and simultaneously is a critical factor investigated by researchers. This study uses Multivariate Adaptive Regression Splines (MARS) to estimate the VTEC approximate model and then substitutes this model in the observation equation to form the normal equation. The least squares method is used to solve the RDCB and VTEC model together. The research findings show that this method has good modeling effectiveness and the estimated RDCB has good reliability. The estimated VTEC model applied to GPS single-frequency precise point positioning has better positioning accuracy in comparison to the IGS global ionosphere map (GIM).