利用GPS技术实时监测上海区域电离层变化

基于GPS技术实时监测电离层变化原理, 利用载波平滑伪距观测值建立区域电离层模型的方法, 计算了电离层延迟量和硬件延迟, 根据硬件延迟值相对稳定的特点, 采取一定时段求解出硬件延迟量, 对实时硬件延迟量进行预报, 进而实时分离GPS信号传播路径上的垂直总电子含量VTEC. 利用上海区域内的GPS网的观测数据, 建立实时上海区域电离层延迟模型, 监测上海区域的电离层变化. 数据分析结果表明, 这种方法的内符合精度优于3 TECU.      

电离层薄层高度对电离层模型化的影响

利用IRI2012模型分析了电离层薄层高度的时空变化规律,提出了基于应用中STEC的电离层改正误差分析理论,分析了电离层薄层高度变化的相关影响.结果表明,电离层薄层高度变化对电离层穿刺点位置、投影映射函数值、电离层建模结果、电离层模型精化和电离层模型精度评估结果的影响较大.高度截止角为10°时,电离层薄层高度变化导致电离层穿刺点的经纬度差异最大可达3.2°,投影映射函数最高可引入约15.46%的误差,电离层建模结果差异和建模实用误差最高分别达9.71%,3.64%,采用不同薄层高度数据的电离层模型参数拟合和模型精化结果最大可引入约9.26%的误差,采用不同电离层薄层高度数据进行模型精度评定时最大可引入约9.62%的误差.根据这些研究结果可知:在实际应用中应采用电离层薄层高度模型,并选取较大的卫星高度截止角来减小薄层高度变化引入的误差;采用固定高度时,区域电离层建模采用与实际电离层薄层一致的固定高度;进行精度评估时,参考数据的电离层薄层高度与需要精度评估的电离层模型薄层高度相等.      

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.     

基于BP神经网络技术的电离层VTEC融合

随着电离层探测技术的不断发展,电离层观测资料也越来越多,只使用单一的观测资料会出现电离层反演精度不高的问题。为了提高电离层的反演精度,使用BP神经网络技术将地基反演和国际参考电离层(international reference ionosphere,IRI)模型的垂直总电子含量（vertical total electron content,VTEC）数据进行有效融合。在温带地区\[35°(N)~45°(N),60°(E)~80°(E)\]进行电离层反演试验,结果表明基于BP神经网络技术的电离层数据融合和地基反演获得的电离层VTEC精度都比较高,但是基于BP神经网络的电离层数据融合反演精度比地基反演更高,所以基于BP神经网络技术的数据融合能够提高电离层的反演精度。     

GNSS电离层模型改正精度评估与分析

电离层时延误差是导航定位信号在空间传播路径上的主要误差源之一,因此全面了解GNSS电离层模型的改正精度具有一定现实意义.根据GPS,BDS和Galileo系统所采用的电离层修正模型,利用2014年电离层校正参数,以高精度全球电离层图为基准,评估分析了三大系统电离层时延的改正精度.结果表明:目前GNSS使用的几种电离层修正模型的改正率在65~75%左右;Galileo系统使用的第二版NeQuick模型与第一版NeQuick模型相比在修正精度上并无显著提高;GPS使用的Klobuchar 8参数模型在北半球25°-45°N的中纬度地区精度很高,但是在全球其他区域精度较低,分布性较差,而NeQuick模型全球改正率分布则较为平均且平滑.      

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.     

国际参考电离层模型输出参数峰值高度性能

电离层峰值高度H_mF₂是描述电离层形态的重要参数之一,国际参考电离层模型IRI-2016中融入了大量电离层测高仪和无线电掩星探测数据,用以提升H_mF₂的预测精度.本文利用太阳活动低年（2007—2010年）气象、电离层和气候卫星联合观测系统COSMIC探测数据描述全球范围内COSMIC H_mF₂的三维形态变化,对比分析了IRI-2016与IRI-2012模型的预测结果,同时分析了IRI-2016模型输出H_mF₂的性能.结果表明,IRI模型在中高纬度地区的输出结果高于COSMIC反演结果,而赤道及低纬地区则大都偏低.与IRI-2012模型相比,IRI-2016模型的输出结果在夜间至凌晨时段呈现较为明显的纬向梯度变化且大部分区域输出值偏高,但在白天时段赤道附近区域的输出值大都偏低.上述结果为电离层IRI模型的完善提供了一定参考.      

高精度的连续电离层延迟建模研究

在多项式电离层模型确定过程中, 针对实测数据出现空白区域的问题, 提出了以GIM 数据作为背景场, 联合实测数据进行多项式电离层模型确定的方法, 分析了两种数据的权重, 建立了相应的数学模型; 另外对模型在时段间的连续性问题, 提出了利用相邻两组系数进行时间加权计算电离层延迟的平滑方法; 最后通过算例验证了建模和平滑方法的有效性.      

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.      

IGS电离层产品在双向时间频率传递中的应用

利用IGS组织提供的全球电离层资料对卫星双向时间频率传递中的电离层误差进行修正。IGS提供特定时刻、固定经纬度网格点上的电离层总电子含量。对该电离层资料首先进行空间四点网格内插,然后利用双线性内插得到电离层穿刺点所需时刻的总电子含量,最后将得到电离层数据经过处理用于双向时间频率传递修正。结果表明：电离层对C波段的影响在（0～0.5）ns范围内,这对亚纳秒量级的时间比对是必须考虑的。IGS提供的电离层产品适合应用于双向时间频率传递,具有方法简单、准确度高和价格低廉等特点。     

Using GPS-TEC data to calibrate VTEC computed with the IRI model over Nigeria

This paper presents the development of a Total Electron Content (TEC) map for the Nigerian ionosphere. In this work, TEC measurements obtained from the AFRL-SCINDA GPS (Air Force Research Laboratory-Scintillation Network Decision Aid, Global Positioning System) equipment installed at Nsukka (6.87°N, 7.38°E) are used to adapt the International Reference Ionosphere (IRI) model for the Nigerian Ionosphere. The map is being developed as a computer program (implemented in the MATLAB programming language) that shows spatial and temporal representations of TEC for the Nigerian ionosphere. The method is aimed at showing how the IRI model can be used to estimate VTEC over wide areas by incorporating GPS measurements. This method is validated by using GPS VTEC data collected from a station in Ilorin (8.50°N, 4.55°E).     

Application of neural networks to South African GPS TEC modelling

The propagation of radio signals in the Earth’s atmosphere is dominantly affected by the ionosphere due to its dispersive nature. Global Positioning System (GPS) data provides relevant information that leads to the derivation of total electron content (TEC) which can be considered as the ionosphere’s measure of ionisation. This paper presents part of a feasibility study for the development of a Neural Network (NN) based model for the prediction of South African GPS derived TEC. The South African GPS receiver network is operated and maintained by the Chief Directorate Surveys and Mapping (CDSM) in Cape Town, South Africa. Vertical total electron content (VTEC) was calculated for four GPS receiver stations using the Adjusted Spherical Harmonic (ASHA) model. Factors that influence TEC were then identified and used to derive input parameters for the NN. The well established factors used are seasonal variation, diurnal variation, solar activity and magnetic activity. Comparison of diurnal predicted TEC values from both the NN model and the International Reference Ionosphere (IRI-2001) with GPS TEC revealed that the IRI provides more accurate predictions than the NN model during the spring equinoxes. However, on average the NN model predicts GPS TEC more accurately than the IRI model over the GPS locations considered within South Africa.     

Regional modeling of the ionosphere using adaptive neuro-fuzzy inference system in Iran

In recent years, new techniques and algorithms such as Artificial Neural Networks (ANNs), Fuzzy Inference Systems (FIS) and Genetic Algorithm (GA) have been used as alternative statistical tools in modeling and forecasting issues. These methods have been extensively used in the field of geosciences and atmospheric physics. The main purpose of this paper is to combine FIS and ANNs for local modeling of the ionosphere Total Electron Content (TEC) in Iran. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed for TEC modeling. Also, Multi-Layer Perceptron ANN (MLP-ANN) and ANN based on Radial Base Functions (RBF) have been designed for analyzing ANFIS results. Observations of 29 Global Positioning System (GPS) stations from the Iranian Permanent GPS Network (IPGN) have been used in 3 different seasons in 2015 and 2016. These stations are located at geomagnetic low latitudes region. Out of these 29 stations, 24 stations for training and 5 stations for testing and validating were selected. The relative and absolute errors have been used to evaluate the accuracy of the proposed model. Also, the results of this paper are compared with the International Reference Ionosphere model (IRI2016). The maximum values of the average relative error for RBF, MLP-ANN, ANFIS and IRI2016 methods are 13.88%, 11.79%, 10.06%, and 18.34%, respectively. Also, the maximum values of the average absolute error for these methods are 2.38, 2.21, 1.5 and 3.36 TECU, respectively. Comparison of diurnal predicted TEC from the ANFIS, RBF, MLP-ANN and IRI2016 models with GPS-TEC revealed that the ANFIS provides more accurate predictions than the other methods in the test area.     

Tomographic reconstruction of the ionospheric electron density as a function of space and time

Electron density distribution is the major determining parameter of the ionosphere. Computerized Ionospheric Tomography (CIT) is a method to reconstruct ionospheric electron density image by computing Total Electron Content (TEC) values from the recorded Global Positioning Satellite System (GPS) signals. Due to the multi-scale variability of the ionosphere and inherent biases and errors in the computation of TEC, CIT constitutes an underdetermined ill-posed inverse problem. In this study, a novel Singular Value Decomposition (SVD) based CIT reconstruction technique is proposed for the imaging of electron density in both space (latitude, longitude, altitude) and time. The underlying model is obtained from International Reference Ionosphere (IRI) and the necessary measurements are obtained from earth based and satellite based GPS recordings. Based on the IRI-2007 model, a basis is formed by SVD for the required location and the time of interest. Selecting the first few basis vectors corresponding to the most significant singular values, the 3-D CIT is formulated as a weighted least squares estimation problem of the basis coefficients. By providing significant regularization to the tomographic inversion problem with limited projections, the proposed technique provides robust and reliable 3-D reconstructions of ionospheric electron density.     

A 12 year comparison of MIDAS and IRI 2007 ionospheric Total Electron Content

Data assimilation in conventional meteorological applications uses measurements in conjunction with a physical model. In the case of the ionised region of the upper atmosphere, the ionosphere, assimilation techniques are much less mature. The empirical model known as the International Reference Ionosphere (IRI) could be used to augment data-sparse regions in an ionospheric now-cast and forecast system. In doing so, it is important that it does not introduce systematic biases to the result. Here, the IRI model is compared to ionospheric observations from the Global Positioning System satellites over Europe and North America. Global Positioning System data are processed into hour-to-hour monthly averages of vertical Total Electron Content using a tomographic technique. A period of twelve years, from January 1998 to December 2009, is analysed in order to capture variations over the whole solar cycle. The study shows that the IRI model underestimates Total Electron Content in the daytime at solar maximum by up to 37% compared to the monthly average of GPS tomographic images, with the greatest differences occurring at the equinox. IRI shows good agreement at other times. Errors in TEC are likely due to peak height and density inaccuracies. IRI is therefore a suitable model for specification of monthly averages of Total Electron Content and can be used to initialise a data assimilation process at times away from solar maximum. It may be necessary to correct for systematic deviations from IRI at solar maximum, and to incorporate error estimation into a data assimilation scheme.     

TEC derived from some GPS stations in Nigeria and comparison with the IRI and NeQuick models

Total electron content (TEC) measured simultaneously using Global Positioning System (GPS) ionospheric monitors installed at some locations in Nigeria during the year 2011 (Rz = 55.7) was used to study the diurnal, seasonal, and annual TEC variations. The TEC exhibits daytime maximum, seasonal variation and semiannual variations. Measured TEC were compared with those predicted by the improved versions of the International Reference Ionosphere (IRI) and NeQuick models. The models followed the diurnal and seasonal variation patterns of the observed values of TEC. However, IRI model produced better estimates of TEC than NeQuick at all locations.     

Influence of the 27-day solar flux variations on the ionosphere parameters measured at Irkutsk in 2003–2005

We present an investigation of the influence of the 27-day solar flux variations, caused by solar rotation, on the ionosphere parameters such as the F2 layer critical frequency (foF2) and the total electron content (TEC). Our observational data were obtained with the Irkutsk Digisonde (DPS-4) located at 52.3 North and 104.3 East during the period from 2003 to 2005. In addition, we use TEC data from the Global Ionosphere Maps (GIM) based on Global Positioning System (GPS) satellites. The solar radiation flux at a wavelength of 10.7 cm (F10.7 index) is used as an index characterizing the solar activity level. A good correlation between observed ionosphere parameters and solar activity variations is found especially in autumn-to-winter season. We estimate the impact of the 27-day solar flux variations on the day-to-day variability and determine the time delay of the ionosphere response.     

Multi-GNSS contributions to differential code biases determination and regional ionospheric modeling in China

Due to the limited number and uneven distribution globally of Beidou Satellite System (BDS) stations, the contributions of BDS to global ionosphere modeling is still not significant. In order to give a more realistic evaluation of the ability for BDS in ionosphere monitoring and multi-GNSS contributions to the performance of Differential Code Biases (DCBs) determination and ionosphere modeling, we select 22 stations from Crustal Movement Observation Network of China (CMONOC) to assess the result of regional ionospheric model and DCBs estimates over China where the visible satellites and monitoring stations for BDS are comparable to those of GPS/GLONASS. Note that all the 22 stations can track the dual- and triple-frequency GPS, GLONASS, and BDS observations. In this study, seven solutions, i.e., GPS-only (G), GLONASS-only (R), BDS-only (C), GPS + BDS (GC), GPS + GLONASS (GR), GLONASS + BDS (RC), GPS + GLONASS + BDS (GRC), are used to test the regional ionosphere modeling over the experimental area. Moreover, the performances of them using single-frequency precise point positioning (SF-PPP) method are presented. The experimental results indicate that BDS has the same ionospheric monitoring capability as GPS and GLONASS. Meanwhile, multi-GNSS observations can significantly improve the accuracy of the regional ionospheric models compared with that of GPS-only or GLONASS-only or BDS-only, especially over the edge of the tested region which the accuracy of the model is improved by reducing the RMS of the maximum differences from 5–15 to 2–3 TECu. For satellite DCBs estimates of different systems, the accuracy of them can be improved significantly after combining different system observations, which is improved by reducing the STD of GPS satellite DCB from 0.243 to 0.213, 0.172, and 0.165 ns after adding R, C, and RC observations respectively, with an increment of about 12.3%, 29.4%, and 32.2%. The STD of GLONASS satellite DCB improved from 0.353 to 0.304, 0.271, and 0.243 ns after adding G, C, and GC observations, respectively. The STD of BDS satellite DCB reduced from 0.265 to 0.237, 0.237 and 0.229 ns with the addition of G, R and GR systems respectively, and increased by 10.6%, 10.4%, and 13.6%. From the experimental positioning result, it can be seen that the regional ionospheric models with multi-GNSS observations are better than that with a single satellite system model.     

Evaluation of the improvement of IRI-2016 over IRI-2012 at the India low-latitude region during the ascending phase of cycle 24

This paper investigated the performance of the latest International Reference Ionosphere model (IRI-2016) over that of IRI-2012 in predicting total electron content (TEC) at three different stations in the Indian region. The data used were Global Positioning System (GPS) data collected during the ascending phase of solar cycle 24 over three low-latitude stations in India namely; Bangalore (13.02°N Geographic latitude, 77.57°E Geographic longitude), Hyderabad (17.25°N Geographic latitude, 78.30°E Geographic longitude) and Surat (21.16°N Geographic latitude, 72.78°E Geographic longitude). Monthly, the seasonal and annual variability of GPS-TEC have been compared with those derived from International Reference Ionosphere IRI-2016 and IRI-2012 with two different options of topside electron density: NeQuick and IRI01-corr. It is observed that both versions of IRI (i.e., IRI-2012 and IRI-2016) predict the GPS-TEC with some deviations, the latest version of IRI (IRI-2016) predicted the TEC similar to those predicted by IRI-2012 for all the seasons at all stations except for morning hours (0500 LT to 1000?LT). This shows that the effect of the updated version is seen only during morning hours and also that there is no change in TEC values by IRI-2016 from those predicted by IRI-2012 for the rest of the time of the day in the Indian low latitude region. The semiannual variations in the daytime maximum values of TEC are clearly observed from both GPS and model-derived TEC values with two peaks around March-April and September-October months of each year. Further, the Correlation of TEC derived by IRI-2016 and IRI-2012 with EUV and F10.7 shows similar results. This shows that the solar input to the IRI-2016 is similar to IRI 2012. There is no significant difference observed in TEC, bottom-side thickness (B0) and shape (B1) parameter predictions by both the versions of the IRI model. However, a clear improvement is visible in hmF2 and NmF2 predictions by IRI-2016 to that by IRI-2012. The SHU-2015 option of the IRI-2016 gives a better prediction of NmF2 for all the months at low latitude station Ahmedabad compare to AMTB 2013.     

Regional 4-D modeling of the ionospheric electron density from satellite data and IRI

Accurate knowledge of the electron density is the key point in correcting ionospheric delays of electromagnetic measurements and in studying ionosphere physics. During the last decade Global Navigation Satellite Systems (GNSS) have become a promising tool for monitoring ionospheric parameters such as the total electron content (TEC). In this contribution we present a four-dimensional (4-D) model of the electron density consisting of a given reference part, i.e., the International Reference Ionosphere (IRI), and an unknown correction term expanded in terms of multi-dimensional base functions. The corresponding series coefficients are calculable from the satellite measurements by applying parameter estimation procedures. Since satellite data are usually sampled between GPS satellites and ground stations, finer structures of the electron density are modelable just in regions with a sufficient number of ground stations. The proposed method is applied to simulated geometry-free GPS phase measurements. The procedure can be used, for example, to study the equatorial anomaly.