基于三频数据的北斗卫星导航系统DCB参数精度评估方法

差分码偏差（Differential Code Biases,DCB）参数作为导航电文中重要的一项,是影响用户PNT服务的主要误差源之一。北斗卫星导航系统（后文简称“北斗系统”）发射三个频点的导航信号,在导航电文中需要发播卫星的2个TGD（Timing Group Delay）参数。文章首先介绍了北斗系统卫星DCB参数最小二乘解算与形式误差评估;其次根据北斗系统三频特点,提出了不同频点组合计算垂直方向电离层电子总含量（VTEC）互差的DCB精度定量评估方法,并与IGS(International GNSS Service)提供的GPS卫星DCB精度进行比较;最后,详细分析了DCB参数精度对用户等效距离误差（UERE）计算和定位计算的影响,分别采用卫星出场标定DCB参数和经过解算DCB参数进行评估。实测数据分析结果表明,北斗系统卫星DCB参数解算形式误差与IGS解算GPS卫星DCB参数形式误差相当,但受卫星类型和解算测站的几何分布限制,北斗系统卫星DCB参数解算不确定度相比IGS略差,估计精度优于0.5ns,不同频率组合计算VTEC互差绝对值均值优于0.6TECU。相比采用卫星出场标定值,采用系统解算DCB参数后,双频用户三维位置误差改善13.80%～47.42%。     

GPS电离层反演方法研究及其在地震方面的应用

利用地基GPS数据计算了电离层单球壳模型穿刺点上的垂向总电子含量(VTEC), 根据VTEC和卫星及测站接收机的差分码间偏差DCB的不同时变特性采用了复弧法, 将VTEC作为局部变量(每30 min 一组, 可调), DCB作为一天的全局量进行解算. 在解算的过程中, 充分考虑VTEC的空间分布特性, 利用变异函数通过Kriging插值法建立电离层VTEC的二维格网模型, 并给出了卫星和接收机的差分码间偏差DCB. 通过与IGS结果的比较, 发现其结果可靠, 且时空分辨率和稳定性都有较大提高. 同时, 基于简化的三元样条插值基函数对电离层电子密度进行三维展开, 利用乘型代数重建技术MART算法构建了同批数据的四维层析成像结果, 获得了电离层电子密 度的四维分布. 其结果与CHAMP无线电掩星结果非常一致. 利用上面两种算法又分别对2008年5月长江三角洲地区地基GPS数据进行处理, 简要分析了该时段该地区上空电离层总电子含量和电子密度的变化情况及其对汶川地震的响应.      

MGEX北斗差分码偏差两种精确处理方法对比分析

差分码偏差是北斗卫星导航系统（BDS）在高精度定位和电离层建模中需精确处理的系统误差之一.利用MGEX发布的2017年全年和2018年6月的BDS卫星的差分码偏差数据,比较分析了DLR和CAS分别解算的BDS卫星差分码偏差的日解值、月平均值和稳定性的变化特性.分析结果表明,DLR与CAS估算的BDS卫星差分码偏差值差异不大,具有较好的一致性;2017年CAS估算的BDS卫星C2I-C6I差分码偏差稳定性略优于DLR,C2I-C7I差分码偏差稳定性与DLR相当,且均具有较高稳定性;2018年6月DLR C2I-C6I差分码偏差月平均值稳定性优于CAS;C2I-C7I差分码偏差的稳定性明显优于C2I-C6I差分码偏差,卫星差分码偏差月平均值稳定性优于日解值稳定性.      

基于硬件信号模拟器的电离层TEC监测仪 差分码偏差长期变化

利用硬件信号模拟器可以标定电离层TEC监测仪的差分码偏差.通过对相同接收机时隔近41.5月进行的两次差分码偏差标定实验，以GPS系统为例分析了硬件标定法得到的差分码偏差随时间的长期变化情况.结果表明:接收机差分码偏差均值从第一次实验的16.122ns增加至第二次实验的16.749ns，在约41.5月的时间内增加约0.627ns，月增量为0.0151ns，增加比较缓慢；第二次实验的差分码偏差标准差也有所增加，但增量也不大（均值分别为0.05ns和0.07ns）.此外，两次标定实验的TEC测量精度（均方根误差）均达到约0.3TECU，对应的差分码偏差误差约0.1ns，这说明该接收机差分码偏差变化的一致性较好.若不加以再次标定，第二次实验时TEC测量误差将增加至约1.8TECU，月增量约为0.0434TECU.      

一种用于电离层TEC监测的GNSS信号载波跟踪算法

全球卫星导航系统（GNSS）是电离层TEC监测中应用最普遍的手段. 目前方法通常是在传统导航用途的GNSS接收机输出的原始观测量基础上,经过数据后处理得到电离层TEC信息,其GNSS信号的跟踪处理算法依然采用GNSS导航接收机的算法. 针对GNSS系统用于电离层TEC监测的特殊性,提出一种称为GNSS双频信号和差联合跟踪的新算法,与传统方法相比,该算法直接跟踪电离层TEC的变化,可以提高电离层TEC跟踪的灵敏度和TEC的观测精度,改善电离层TEC监测性能.      

电离层不规则结构对GPS性能的影响

电离层不规则结构的存在可引起无线电信号的幅度和相位发生随机起伏, 这 种电离层闪烁现象会影响全球定位系统(Global Positioning System, GPS)的 性能, 降低定位精度, 严重时导致信号失锁. 电离层不规则结构对GPS性能的 影响涉及电离层物理、接收机设计和表征卫星几何分布的精度衰减因子(Dilution of Positioning, DOP)等多方面因素. 本文通过对表征电离层不规则结构参数 的分析, 根据GPS接收机跟踪环路和闪烁信号模型, 综合研究了电离层闪烁对 GPS接收机载波跟踪环和码跟踪环跟踪误差的影响; 结合实际观测, 评述 了电离层不规则结构对单频和双频GPS接收机定位性能的影响, 在此基础上 提出了有待深入研究的问题及具体建议.      

软件GNSS信号模拟器架构与中频信号生成

软件GNSS(Global Navigation Satellite Systems)信号模拟器对于GNSS接收机的高效研发将做出重要贡献,因其结构灵活、开放性以及低成本.以GPS/Galileo组合系统为例,讨论了软件GNSS中频信号模拟器的架构,主要功能模块包括卫星星座仿真、接收机轨迹生成、传播通道特性仿真(包括电离层模型、对流层模型、多径模型等)、数字中频信号生成.在此基础上,着重阐述了数字中频信号生成模块的实现,功率谱图及分析结果验证了所生成的信号,包括GPS L1 C/A,Galileo E1 CBOC(Composite Binary Offset Carrier),Galileo E5a和E5b信号.     

GNSS模拟器中频调制卡设计与实现

GNSS(Global Navigation Satellite System)信号模拟器能够根据用户所设置的GNSS系统和信号形式、载体动态和环境参数,精确模拟出载体收到的卫星信号,这为GNSS系统级仿真试验和接收机的测试提供一种高效的工具.主要研究兼容多系统多频点的卫星信号模拟器中频信号发生器和数字信号处理技术,提出了数字合路与功率控制的方法和信号相位精确模拟的途径;在基于PCIE+DSP+FPGA+DAC架构的中频板卡上完成了与PC通信、波形控制参数计算和更新、基带信号调制以及模拟中频信号的产生;最后给出了与相应的GNSS接收机的对接结果,验证了所产生中频信号的正确性和信号质量.     

北斗区域Klobuchar改进模型及其修正精度分析

北斗卫星导航系统采用Klobuchar模型修正单频接收机用户的电离层延迟误差,由于此模型从亚洲地区应用角度考虑,在某一特定区域的修正精度甚至不足50%。为进一步提高区域电离层延迟修正精度,提出在原模型8个改正参数的基础上增加5个关键参数的Klobuchar改进模型,并采用松弛迭代与直线搜索法中的黄金分割相结合的算法对新增参数进行求解。以天津及其附近区域为例,利用GPStation6接收机采集到的实测数据对改进模型与原模型进行计算。将国际全球导航卫星系统服务组织（International GNSS Service,IGS）发布的全球电离层格网数据作为参考值,对比分析改进模型与原模型的修正精度。结果表明,区域Klobuchar改进模型在天津及其附近区域的电离层延迟平均修正精度比原模型提升了10.46%,平均修正精度达到77.51%。     

基于GNSS的高轨卫星定位技术研究

利用全球卫星导航系统(GNSS)进行导航定位具有全球、全天候、实时和高精度的优点,应用于高地球轨道(HEO)卫星的定位,能够提供精确的轨道和姿态确定,并且可以克服目前主要利用地面测控系统对HEO卫星进行定位的设备复杂、投资高等缺点,使得自主导航成为可能.本文对利用GNSS的高轨卫星定位相关技术进行了研究,分析了单一GNSS系统和多个GNSS组合系统的卫星可见性、动态性和几何精度因子(GDOP).通过仿真分析表明,利用组合GNSS系统并通过提高GNSS接收机灵敏度的方法,可以解决GNSS进行HEO卫星定位的相关问题,并能保证HEO卫星定位精度的要求.      

Differential code bias estimation based on uncombined PPP with LEO onboard GPS observations

Differential Code Bias (DCB) is an essential correction that must be provided to the Global Navigation Satellite System (GNSS) users for precise position determination. With the continuous deployment of Low Earth Orbit (LEO) satellites, DCB estimation using observations from GNSS receivers onboard the LEO satellites is drawing increasing interests in order to meet the growing demands on high-quality DCB products from LEO-based applications, such as LEO-based GNSS signal augmentation and space weather research. Previous studies on LEO-based DCB estimation are usually using the geometry-free combination of GNSS observations, and it may suffer from significant leveling errors due to non-zero mean of multipath errors and short-term variations of receiver code and phase biases. In this study, we utilize the uncombined Precise Point Positioning (PPP) model for LEO DCB estimation. The models for uncombined PPP-based LEO DCB estimation are presented and GPS observations acquired from receivers onboard three identical Swarm satellites from February 1 to 28, 2019 are used for the validation. The results show that the average Root Mean Square errors (RMS) of the GPS satellite DCBs estimated with onboard data from each of the three Swarm satellites using the uncombined PPP model are less than 0.18 ns when compared to the GPS satellite DCBs obtained from IGS final daily Global Ionospheric Map (GIM) products. Meanwhile, the corresponding average RMS of GPS satellite DCBs estimated with the conventional geometry-free model are 0.290, 0.210, 0.281 ns, respectively, which are significantly larger than those obtained with the uncombined PPP model. It is also noted that the estimated GPS satellite DCBs by Swarm A and C satellites are highly correlated, likely attributed to their similar orbit type and space environment. On the other hand, the Swarm receiver DCBs estimated with uncombined PPP model, with Standard Deviation (STD) of 0.065, 0.037 and 0.071 ns, are more stable than those obtained from the official Swarm Level 2 products with corresponding STD values of 0.115, 0.101, and 0.109 ns, respectively. The above indicates that high-quality DCB products can be estimated based on uncombined PPP with LEO onboard observations.     

Estimation and analysis of GPS receiver differential code biases using KGN in Korean Peninsula

The total electron content (TEC) estimation by the Global Positioning System (GPS) can be seriously affected by the differential code biases (DCB), referred to as inter-frequency biases (IFB), of the satellite and receiver so that an accuracy of GPS–TEC value is dependent on the error of DCBs estimation. In this paper, we proposed the singular value decomposition (SVD) method to estimate the DCB of GPS satellites and receivers using the Korean GPS network (KGN) in South Korea. The receiver DCBs of about 49 GPS reference stations in KGN were determined for the accurate estimation of the regional ionospheric TEC. They obtained from the daily solution have large biases ranging from +5 to +27 ns for geomagnetic quiet days. The receiver DCB of SUWN reference station was compared with the estimates of IGS and JPL global ionosphere map (GIM). The results have shown comparatively good agreement at the level within 0.2 ns. After correction of receiver DCBs and knowing the satellite DCBs, the comparison between the behavior of the estimated TEC and that of GIMs was performed for consecutive three days. We showed that there is a good agreement between KASI model and GIMs.     

Estimation of QZSS differential code biases using QZSS/GPS combined observations from MGEX

As an important error source in Global Navigation Satellite System (GNSS) positioning and ionospheric modeling, the differential code biases (DCB) need to be estimated accurately, e.g., the regional Quasi-Zenith satellite system (QZSS). In this paper, the DCB of QZSS is estimated by adopting the global ionospheric modeling method based on QZSS/GPS combined observations from Multi-GNSS experiment (MGEX). The performance of QZSS satellite and receiver DCB is analyzed with observations from day of year (DOY) 275–364, 2018. Good agreement between our estimated QZSS satellite DCB and the products from DLR and CAS is obtained. The bias and root mean square (RMS) of DCB are mostly within ±0.3 ns. The day-to-day fluctuation of the DCB time series is less than 0.5 ns with about 96% of the cases for all satellites. However, the receiver DCB is a little less stable than satellite DCB, and their standard deviations (STDs) are within 1.9 ns. The result shows that the stability of the receiver DCBs is not significantly related to the types of receiver or antenna.     

Estimation of differential code biases with Jason-2/3 onboard GPS observations

With the continuous deployment of Low Earth Orbit (LEO) satellites, the estimation of differential code biases (DCBs) based on GNSS observations from LEO has gained increasing attention. Previous studies on LEO-based DCB estimation are usually using the spherical symmetry ionosphere assumption (SSIA), in which a uniform electron density is assumed in a thick shell. In this study, we propose an approach (named the SHLEO method) to simultaneously estimate the satellite and LEO onboard receiver DCBs by modeling the distribution of the global plasmaspheric total electron content (PTEC) above the satellite orbit with a spherical harmonic (SH) function. Compared to the commonly used SSIA method, the SHLEO model improves the GPS satellite DCB estimation accuracy by 13.46% and the stability by 22.34%, respectively. Compared to the GPS satellite DCBs estimated based on the Jason-3-only observations, the accuracy and monthly stability of the satellite DCBs can be improved by 14.42% and 26.8% when both Jason-2 and Jason-3 onboard observations are jointly processed. Compared with the Jason-2 solutions, the GPS satellite DCB estimates based on the fusion of Jason-2 and Jason-3 observations have an improved consistency of better than 18.26% and 9.71% with the products provided by the Center for Orbit Determination in Europe (CODE) and Chinese Academy of Sciences (CAS). Taking the DCB products provided by the German Aerospace Center (DLR) as references, there is no improvement in accuracy of the GPS satellite DCB estimates based on the fusion of Jason-2 and Jason-3 observations than the Jason-2 solutions alone. A periodic variation is found in the time series of both the Jason-3 and Jason-2 onboard receiver DCB estimates. Preliminary analysis of the PTEC distribution based on the estimated SH coefficients are also presented.     

The multi-source data fusion global ionospheric modeling software—IonoGim

We introduce a new global ionospheric modeling software—IonoGim, using ground-based GNSS data, the altimetry satellite and LEO (Low Earth Orbit) occultation data to establish the global ionospheric model. The software is programmed by C++ with fast computing speed and highly automatic degree, it is especially suitable for automatic ionosphere modeling. The global ionospheric model and DCBs obtained from IonoGim were compared with the CODE (Center for Orbit Determination in Europe) to verify its accuracy and reliability. The results show that IonoGim and CODE have good agreement with small difference, indicating that IonoGim owns high accuracy and reliability, and can be fully applicable for high-precision ionospheric research. In addition, through comparison between only using ground-based GNSS observations and multi-source data model, it can be demonstrated that the space-based ionospheric data effectively improve the model precision in marine areas where the ground-based GNSS tracking station lacks.     

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.      

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.     

Calibration errors in determining slant Total Electron Content (TEC) from multi-GNSS data

The global navigation satellite system (GNSS) is presently a powerful tool for sensing the Earth's ionosphere. For this purpose, the ionospheric measurements (IMs), which are by definition slant total electron content biased by satellite and receiver differential code biases (DCBs), need to be first extracted from GNSS data and then used as inputs for further ionospheric representations such as tomography. By using the customary phase-to-code leveling procedure, this research comparatively evaluates the calibration errors on experimental IMs obtained from three GNSS, namely the US Global Positioning System (GPS), the Chinese BeiDou Navigation Satellite System (BDS), and the European Galileo. On the basis of ten days of dual-frequency, triple-GNSS observations collected from eight co-located ground receivers that independently form short-baselines and zero-baselines, the IMs are determined for each receiver for all tracked satellites and then for each satellite differenced for each baseline to evaluate their calibration errors. As first derived from the short-baseline analysis, the effects of calibration errors on IMs range, in total electron content units, from 1.58 to 2.16, 0.70 to 1.87, and 1.13 to 1.56 for GPS, Galileo, and BDS, respectively. Additionally, for short-baseline experiment, it is shown that the code multipath effect accounts for their main budget. Sidereal periodicity is found in single-differenced (SD) IMs for GPS and BDS geostationary satellites, and the correlation of SD IMs over two consecutive days achieves the maximum value when the time tag is around 4?min. Moreover, as byproducts of zero-baseline analysis, daily between-receiver DCBs for GPS are subject to more significant intra-day variations than those for BDS and Galileo.     

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.