自相关分析法用于电离层TEC的内插评估

基于2004年实测数据的统计分析,将自相关分析法用于电离层TEC的缺值内插,并进行精度评估.采用上海地区GPS综合应用网和中国地壳运动GPS监测网数据,解算成电离层垂直TEC,对缺值进行了时序内插及评估.结果表明,缺值段内的插值误差一般中间较大,两侧较小,插值误差远小于均方差.将自相关分析法内插结果与线性插值法、抛物线法、三次样条法内插结果进行对比,发现对于缺值较多、变化较复杂的缺值段,其插值精度有明显的提高.采用自相关方法进行内插后,有效减小了由于缺值而引起的局部跳跃变化,可以比较准确地研究TEC变化特性.      

厦门电离层垂测仪电子浓度剖面最优插值同化的初步建立

最优插值(Optimal Interpolation, OI)同化是一种利用最小二乘法使分析误差方差极小化的方法. 提出并建立了一种电离层一维剖面最优插值的同化方法. 借鉴及参考气象的同化方法, 基于最优插值方法, 采用厦门电离层垂测仪2009年6至8月观测数据和一维电离层理论模式(IGGCAS1D)背景值来构建误差协方差, 并进行同化试验和分析预报. 结果表明方法可靠, 没有出现同化时背景数据与观测数据偏离较大的问题, 同化后所得结果与观测数据符合较好.      

全球电离层地图TEC数据的插值算法

由IGS工作组提供的全球电离层地图(GIM)是电离层重要的应用数据.卫星高度计能够提供全球实时的电离层延迟误差校正.利用GIM数据,以Jason-3时空分辨率进行电离层总电子含量(TEC)的时间维度插值和空间维度插值,其中空间维度插值采用了Kriging插值和双线性插值两种方法.针对两种插值方法得到的总电子含量,与平滑...     

新版亚大地区F2电离层频率预测方法

新版亚大地区F2电离层频率预测方法根据电离层参量与太阳黑子数12个月流动平均值R12的互相关原理, 使用R12代替了由重庆F2层临界频率导出的电离层指数Ic, 减小了R12转换为Ic时的误差; 并在原亚大地区F2电离层频率预测方法的基础上审慎地选择并增补了一些探测站的大量数据, 不再使用三组不 同R12值的B系数去插值, 避免了中间环节所带来的插值误差. 并对几种不同方法的预测结果进行了比较, 结果显示新版本的预测精度较高.      

卡尔曼滤波和自相关分析方法短期预报电离层f0F2的比较

电离层参数f₀F₂的预报是电离层研究的一个重要方向.本文选取2011年北京、长春、青岛和苏州四个常规观测站的f₀F₂数据,分别采用卡尔曼滤波、自相关分析法和国际参考电离层模型（IRI）对f₀F₂实施短期预报（提前1h预报）,并通过与实际观测数据的对比,对三种方法预报f₀F₂的性能进行了比较.研究结果表明,在磁平静时期,采用卡尔曼滤波方法进行f₀F₂预报的均方误差为0.532MHz,相对误差8.11%,比国际电离层参考模型的均方误差和相对误差分别降低1.47MHz和14.58%;采用自相关分析方法进行f₀F₂预报的均方根误差为0.967MHz,相对误差11.46%,比国际电离层参考模型的均方误差和相对误差分别降低1.035MHz和11.23%.比较结果说明二者对f₀F₂短期预报的精度相对于国际电离层参考模型均有大幅提升.对磁暴期间三种方法的预测性能做了进一步比较,试验结果表明卡尔曼滤波短期预报性能总体上优于自相关分析法,这为f₀F₂短期预报的方法选择提供了一定指导.      

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.      

基于深度学习递归神经网络的电离层总电子含量经验预报模型

利用行星际太阳风参数与太阳活动指数、地磁活动指数、电离层总电子含量格点化地图数据,首次基于一种能处理时间序列的深度学习递归神经网络（Recurrent Neural Network,RNN）,建立提前24h的单站电离层TEC预报模型.对北京站（40°N,115°E）的预测结果显示,RNN对扰动电离层的预测误差低于反向传播神经网络（Back Propagation Neural Network,BPNN）0.49～1.46TECU,将太阳风参数加入预报因子模型后对电离层正暴预测准确率的提升可达16.8%.RNN对2001和2015年31个强电离层暴预报的均方根误差比BPNN低0.2TECU,将太阳风参数加入RNN模型可使31个事件的平均预报误差降低0.36～0.47TECU.研究结果表明深度递归神经网络比BPNN更适用于电离层TEC的短期预报,且在预报因子中加入太阳风数据对电离层正暴的预报效果有明显改善.      

北斗星基增强系统性能评估

以实际广播星历、精密星历和北斗星基增强系统(BDSBAS)增强报文为实验数据，通过计算BDSBAS轨道误差、卫星钟差、空间信号测距误差和BDSBAS格网电离层有效点、播发时间和电离层延迟误差6个指标，评估分析了BDSBAS空间信号的性能。结果显示：BDSBAS增强后的GPS卫星轨道误差在切向、法向、径向分别降低了34.57%,40.57%,30.90%；卫星钟差均方根降低了24.31%，卫星钟差标准差降低了16.8%；空间信号测距误差相比增强前降低了32.75%;BDSBAS格网电离层有效点覆盖了中国及周边地区；BDSBAS各点电离层延迟播发间隔均达到ICAO对精确差分定位的要求；电离层延迟在0°-5°N范围内误差在0.4 m以上，可信度均达到99.9%，在5°-55°N范围内误差小于0.4 m，可信度均为100%;BDSBAS水平定位误差提升超过25%，垂直定位误差提升超过50%，完好性均在99.9%以上。     

中国区域电离层异常数据野值检测

针对中国区域连续运行参考站接收机野值会干扰星基增强系统（SBAS）电离层异常事件提取的问题,提出了一种基于电离层垂直延迟时间梯度的野值检测方法。首先,介绍了电离层延迟数据的提取方法;然后,论述了依据野值和电离层异常的不同时空相关特性进行野值检测的方法,并用单双频定位误差结果验证了野值检测的正确性;最后,对检测结果进行了分析。结果表明:该方法可有效区分野值和电离层异常;在中国大陆构造环境监测网络200多个参考站中,野值检测所剔除的参考站数目在3~10个,对电离层穿透点空间分布的影响在可接受范围内。      

实时电离层格网数据精度评估

电离层延迟是制约单频接收机定位精度的重要误差源之一.为提高单频接收机的实时电离层改正精度,需要实时电离层数据.以中国科学院空天信息创新研究院提供的实时电离层数据为例,对比分析不同太阳活动期实时电离层数据及预报电离层数据与IGS最终电离层数据之间的差值以及不同太阳活动期、不同纬度测站的电离层数据对电离层延迟进行改正后得到的定位精度.结果表明:在低太阳活动期和高太阳活动期,实时电离层数据无法很好地反映大部分海洋上空的电离层变化特性;对不同太阳活动期,实时电离层数据在高纬度测站的定位精度优于预报数据和广播模型,在中纬度测站的定位精度略低于预报数据而与广播模型定位精度相当,在低纬度测站的定位精度略优于预报数据和广播模型.      

An improved approach to model ionospheric delays for single-frequency Precise Point Positioning

PPP with low-cost, single-frequency receivers has been receiving increasing interest in recent years because of its large amount of possible users. One crucial issue in single-frequency PPP is the mitigation of ionospheric delays which cannot be removed by combining observations on different frequencies. For this purpose, several approaches have been developed, such as, the approach using ionospheric model corrections with proper weight, the GRAPHIC (Group and Phase Ionosphere Calibration) approach, and the method to model ionospheric delays over a station with a low polynomial or stochastic process. From our investigation on the stochastic characteristics of the ionospheric delay over a station, it cannot be precisely represented by either a deterministic model in the form of a low-order polynomial or a stochastic process for each satellite, because of its strong irregular spatial and temporal variations. Therefore, a novel approach is developed accordingly in which the deterministic representation is further refined by a stochastic process for each satellite with an empirical model for its power density. Furthermore, ionospheric delay corrections from a constructed model using GNSS data are also included as pseudo-observations for a better solution. A large data set collected from about 200 IGS stations over one month in 2010 is processed with the new approach and several commonly adopted approaches for validation. The results show its significant improvements in terms of positioning accuracy and convergence time with a negligible extra processing time, which is also demonstrated by data collected with a low-cost, handheld, single-frequency receiver.     

基于半无码的P(Y)码自相关GNSS-R海面测高方法

针对传统GPS C/A码延迟测高方法测高精度低和互相关测高方法时延波形信噪比低的问题,研究了基于半无码的P（Y）码自相关海面测高方法。首先,通过理论测高精度模型,对比分析了C/A码和P（Y）码的测高精度;然后,描述了L1、L2双频反射信号基于半无码的P（Y）码自相关处理架构,并论述了测高模型和L1、L2双频电离层时延误差修正方法;最后,利用模拟的L1、L2反射信号对比分析了基于半无码的P（Y）码自相关、C/A码自相关以及信号互相关方法的海面测高精度。结果表明:相比于C/A码自相关和信号互相关测高精度,基于半无码的P（Y）码自相关的测高精度分别提高了3.97倍和1.47倍。      

A comparative study of decision tree,random forest,and convolutional neural network for spread-F identification

Ionospheric spread-F (SF) is a commonly observed phenomenon of electron density perturbation in the F-layer. The ionospheric irregularities structure has an adverse effect on the propagation of electromagnetic waves in the ionosphere. The automatic identification of ionospheric spread-F and statistical study of the formation of spread-F are of great significance to the study of the physical mechanism of ionospheric inhomogeneity and for prediction of ionospheric irregularities. In this paper, we describe and implement three automatic identification methods of spread-F based on machine learning: decision tree, random forest, and convolutional neural network (CNN). The performance of these automatic identification methods was verified using a large set of test data. Results show that the accuracy of all three methods on identifying ionograms with spread-F exceeded 90%. After comparing the results of the three methods, we found that the decision tree method was the simplest and with the structure easiest to be understood, and it required the shortest interpretation time. In terms of the identification results, the random forest method provided better results than the decision tree method, and the CNN method was the best at accurately identifying ionograms with spread-F.     

三频信标高精度TEC测量新方法

电子总含量(TEC)是电离层探测的主要参量之一, 作为层析(CT)的输入参量, TEC测量精度直接影响电离层CT成像的结果. 过去主要采用双频信标测量TEC, 由于相位积分常数的求解、系统硬件延迟等误差, 使得TEC测量结果不能满足电离层CT高精度重建成像的要求. 三频相干信标技术的出现, 使得电离层天基测量技术有了新的发展. 提出了基于三频信标的传播时延-相位联合测量反演TEC的方法, 融合三频信标在电子密度随机起伏探测和相位积分常数计算两方面的优势, 进一步提高了TEC的测量精度. 模拟结果显示利用此方法的三频信标TEC测量结果提高了电离层CT的精度.      

Removing Solar Radiation Based on the Empirical Mode Decomposition Method for Seismo-Ionospheric Anomaly Before the M9.0 Tohoku Earthquake

The ionospheric effect from solar activity can be seen as the background in the process of detecting the ionospheric precursor prior to strong earthquakes. The ionospheric variation induced by the forthcoming earthquake can be covered by the strong solar background during the period of high solar activity. The issue of how to remove the ionospheric effect from solar radiation is of outstanding significance. In this paper, a method of Empirical Mode Decomposition (EMD) is used to eliminate the solar background. As a case study, the global ionospheric map TEC before the M9.0 Tohoku earthquake on 11 March 2011 is analyzed. After the effect of solar radiation is removed using the EMD method, the precursor of the imminent earthquake is more obvious. The ionospheric anomaly had a local character and only appeared close to the earthquake epicenter while the useful signals were covered by the solar radiation background with traditional method, which implies that the EMD method is effective in eliminating solar radiation background.      

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.      

Proposal for ionospheric tomography technique using a new HF radio source

Measurements of the line integral of the electron density along satellite-to-ground ray paths (i.e. TEC) using differential phase or Doppler of two coherent VHF/UHF signals transmitted from NNSS or GPS satellite networks have been used in the ionospheric tomography for mapping large-scale ionospheric images over region of interest. In this paper, we present theoretical studies of using a new signal source HF frequency in tomographic imaging. The initial phase problem inherent in the phase measurement can be eliminated by measuring Faraday rotations. Relative rotation on two adjacent HF frequencies is used to solve the ambiguity problem. A second-order approximation of the Faraday rotation incorporated with ray-tracing technique improves the reconstruction degradation due to ionospheric refractions. CASSIOPE is a multi-purpose small satellite that receives HF signals from ground radar facilities and it is scheduled for launch in early 2008. Simulations have demonstrated the potential applications of the ionospheric tomography in CASSIOPE/e-POP satellite experiment.