基于河北省CORS的区域电离层模型精度分析

为提高区域电离层模型和导航定位服务的精度,利用河北省连续运行参考站系统（CORS） 6个基准站的GPS卫星观测数据进行区域电离层建模和接收机差分码偏差（DCB）估计,并引入中国科学院（CAS）发布的电离层产品内插得到的垂直总电子含量（VTEC）进行区域电离层模型精度验证。实验结果表明,估计的单日GPS卫星DCB与产品值精度相当,偏差控制在0.5 ns以内;河北省CORS站GPS系统接收机DCB稳定性较好,5 d的标准偏差均小于0.1 ns;利用河北省CORS建立的区域电离层TEC在地磁平静期与磁暴期均与CAS产品值具有较高的一致性,TEC偏差控制在2 TECU以内。河北省区域电离层模型能有效监测电离层TEC在不同地磁状态下的时空变化,提高区域导航定位服务水平。     

利用北斗观测实验网解算北斗卫星差分码偏差

差分码偏差(DCB)是电离层总电子含量(TEC)监测和建模的主要系统误差,卫星DCB也是卫星导航系统导航电文的重要参数。研究了卫星DCB的估计算法,推导了不同基准下DCB的转换公式,利用北斗观测实验网解算了2013年北斗卫星的DCB。在同一基准下分析了北斗卫星DCB的稳定性,并与MGEX发布的DCB产品进行了比较分析。实验结果表明,该方法解算的北斗卫星B1-B2DCB在-9~17ns之间,北斗卫星DCB的稳定性优于0.4ns;北斗倾斜地球同步轨道卫星(IGSO)卫星稳定性优于地球静止轨道卫星(GEO)和中圆地球轨道卫星(MEO);利用北斗观测实验网解算的北斗卫星DCB与MGEX解算结果存在最大约1.7ns的系统偏差,可能由于测距码的不一致性所致;接收机硬件材质的不同是导致接收机DCB差异的主要影响因素。     

附加闭合差约束的BDS频间偏差估计模型

中国的北斗卫星导航系统(BeiDou navigation satellite system,BDS)是全球导航卫星系统(global navigation satellite system,GNSS)中唯一全星座提供3频信号的卫星导航系统,其信号频率间共存在3种频间偏差(differential code bias,DCB),分别是DCB_(C2I-C7I)、DCB_(C2I-C6I)、DCB_(C7I-C6I)。理论上,这3种DCB之间存在代数和为零的关系。基于3种频间偏差闭合差约束,加入DCB观测方程,以北斗中轨道(medium earth orbit,MEO)和倾斜地球同步轨道(inclined geosynchronous satellite orbit,IGSO)卫星作为参考卫星,采用附加限制条件的间接平差方法同步估计BDS的3种DCB。选取2018年1月1日—30日多模GNSS实验(multi-GNSS experiment,MGEX)基准站的BDS 3频数据,分别采用附加闭合差约束估计和独立求解两种方法计算北斗二代卫星的3种频间偏差。以中国科学院(Chinese Academy of Sciences,CAS)和德国宇航中心(Deutsches Zentrum für Luft-und Raumfahrt,DLR)的DCB产品作为参考,分析了所提方法估计的DCB精度、稳定性及部分典型卫星的DCB时间序列,验证了所提方法对北斗3频DCB估计的适用性和科学性,并通过BDS单频标准单点定位(standard point positioning,SPP)实验验证了DCB对单点定位精度的影响效果。     

北斗三号卫星导航信号接收机端伪距偏差建模与验证

接收机端伪距偏差是指非理想的卫星导航信号在接收机前端带宽和相关器间隔不同时产生的伪距测量系统性偏差。研究表明,北斗二号、GPS和Galileo系统均存在与接收机类型相关的伪距偏差,影响基于混合类型接收机站网的精密数据处理。本文基于iGMAS网和MGEX网观测数据,采用MW组合、伪距残差和伪距无几何距离无电离层组合3种方法分析北斗三号接收机端伪距偏差特性。试验结果表明,北斗三号同样存在与接收机类型相关的伪距偏差,且无电离层组合的伪距偏差可以达到6 ns。根据偏差特性,按接收机类型建立了8类伪距偏差改正模型。将上述模型应用于卫星差分码偏差(DCB)估计与单频伪距单点定位,结果表明,模型改正后可以显著提升不同接收机类型估计的卫星DCB一致性,其中基于iGMAS网和MGEX网两个不同接收机站网估计得到的北斗三号C2I-C6I、C1P-C5P和C2I-C7D DCB差值分别平均降低了91.6%、64.7%和71.9%;模型改正后单频伪距单点定位水平方向和高程方向精度分别提升了13.9%和11.0%。     

GIM和不同约束条件相结合的BDS差分码偏差估计

现阶段BDS卫星和地面跟踪站数量较少,用BDS单系统获取的DCB精度有限,针对此问题,本文基于CODE GIM,采用两种不同的"零均值"基准约束方案(分别称为约束1和约束2),选取2015年(DOY002-090)MGEX的BDS数据,求解BDS的DCB,并对其进行精度评估。结果表明,两种约束方案下,卫星DCB差值整体趋势一致,DCBC2I-C7I、DCBC2I-C6I的系统性偏差分别约为-3.3ns和1.2ns,接收机DCB的系统性偏差与卫星DCB大小相同,符号相反。相对于约束1,施加约束2后,IGSO和MEO卫星DCB估值更加稳定(DCBC2I-C7ISTD最大改善21%,DCBC2I-C6ISTD最大改善13%),IGSO和MEO卫星的稳定性(分别在0.1ns和0.2ns左右)优于GEO卫星(0.150.32ns)。约束2的DCB估值效果不仅与CAS/DLR产品有较好的一致性(Bias:-0.40.2ns),而且顾及了BDS卫星DCB间的稳定性差异。两种约束方案下,BDS接收机DCB的STD无明显变化,说明约束的选择对BDS接收机DCB的稳定性无明显影响。BDS接收机DCB稳定性整体上优于1ns,中高纬度区域较好(STD 0.4ns左右),低纬度区域稍差(STD 0.81ns)。     

北斗系统硬件延迟解算及精度分析

差分码偏差(differential code bias,DCB)又称硬件延迟,是影响用户导航定位授时(pointing navigation timing,PNT)服务的主要误差源之一。GPS卫星的硬件延迟通常是在电离层建模过程中和电离层模型系数一起解得的,但是北斗系统目前仅是一个区域导航定位系统,无法通过单系统获得高精度的硬件延迟解。提出通过联合GPS和北斗卫星观测数据用低阶球谐模型建模的方式确定北斗卫星和接收机的DCB。实验数据表明在现有条件下采用该方式解算北斗卫星的DCB的精度在0.3ns左右,稳定性较好,且北斗地球静止轨道卫星(GEO)、倾斜同步轨道(IGSO)卫星DCB稳定性好于中轨道(MEO)卫星,北斗卫星DCB的稳定性要优于接收机。     

北斗三号卫星差分码偏差稳定性分析及其对单点定位的影响

差分码偏差（differential code bias,DCB）是影响电离层监测和导航定位精度的重要因素之一,建立DCB改正模型对高精度定位有重要意义。针对北斗三号卫星的广播星历和精密星历钟差参数时间基准不统一的问题,首先介绍了多星座实验（multi-GNSS experiment,MGEX）发布的DCB产品的估计方法,给出了部分DCB产品的精度评估和分析结果;然后提出了北斗三号卫星单频和双频伪距单点定位以及双频精密单点定位的DCB改正模型;最后利用5个MGEX测站连续5 d的实测数据分别进行了DCB改正前后的定位实验。结果表明,MGEX发布的DCB产品均具有较高的稳定性,经卫星DCB改正后,单频和双频伪距单点定位的定位精度分别提高了48%~85%和71%~91%,双频静态精密单点定位的收敛时间减少了56%~83%。     

随机模型对北斗DCB估计和电离层建模的影响

差分码偏差(DCB)作为电离层建模和导航定位中一项重要的误差源,对其进行估计求解至关重要. 为提高北斗卫星导航系统(BDS) DCB估计和电离层建模精度,提出了一种综合高度角、卫地距和测站纬度多因素的随机模型,并对比分析了不同随机模型对BDS DCB估计和电离层垂直总电子含量(VTEC)建模精度的影响. 结果表明:不同随机模型对卫星端DCB解算产生约0.2 ns差异. 相较于高度角随机模型,采用高度角、卫地距组合模型测站DCB估计精度平均提高0.13 ns,电离层建模精度提高了约0.2 TECU. 新提出的随机模型,在低纬度测站DCB解算精度上差于高度角模型和高度角、卫地距组合模型,但在高纬度测站DCB解算结果上更优,且对电离层VTEC建模精度提升效果明显,与前两种随机模型相比分别提升了0.88 TECU和0.68 TECU.      

基于BDS/GPS观测的北斗硬件延迟解算

研究了联合BDS/GPS观测数据基于球冠谐函数的中国区域电离层建模,并精确估计了北斗卫星和接收机DCB。联合解算得到的GPS卫星DCB相对CODE精度优于0.2 ns,GPS接收机DCB相对CODE精度优于1 ns;联合解算得到的中国区域上空VTEC相对CODE事后产品的精度可达2~3 TECU。     

一种北斗卫星差分码偏差估计方法

卫星码偏差会降低卫星测量精度,因此本文就北斗卫星差分码偏差估计进行了研究和验证。首先将电离层作为一个单层,采用球谐函数来参数化电离层TEC值,然后利用最小二乘估算了北斗卫星的码偏差,根据北斗系统2014年4月1-29日间的实测数据计算了14颗北斗卫星的码偏差,最后将计算结果与IGS发布的码偏差参考值进行了对比分析。结果显示误差值在0~2 ns之间,符合度极高,从而验证了该估计方法的有效性。     

Estimation and analysis of Galileo differential code biases

When sensing the Earth’s ionosphere using dual-frequency pseudorange observations of global navigation satellite systems (GNSS), the satellite and receiver differential code biases (DCBs) account for one of the main sources of error. For the Galileo system, limited knowledge is available about the determination and characteristic analysis of the satellite and receiver DCBs. To better understand the characteristics of satellite and receiver DCBs of Galileo, the IGGDCB (IGG, Institute of Geodesy and Geophysics, Wuhan, China) method is extended to estimate the satellite and receiver DCBs of Galileo, with the combined use of GPS and Galileo observations. The experimental data were collected from the Multi-GNSS Experiment network, covering the period of 2013–2015. The stability of both Galileo satellite and receiver DCBs over a time period of 36 months was thereby analyzed for the current state of the Galileo system. Good agreement of Galileo satellite DCBs is found between the IGGDCB-based DCB estimates and those from the German Aerospace Center (DLR), at the level of 0.22 ns. Moreover, high-level stability of the Galileo satellite DCB estimates is obtained over the selected time span (less than 0.25 ns in terms of standard deviation) by both IGGDCB and DLR algorithms. The Galileo receiver DCB estimates are also relatively stable for the case in which the receiver hardware device stays unchanged. It can also be concluded that the receiver DCB estimates are rather sensitive to the change of the firmware version and that the receiver antenna type has no great impact on receiver DCBs.     

一种精确估计区域北斗接收机硬件延迟的方法

提出了一种精确估计区域北斗接收机硬件延迟(DCB)的方法。该方法不需要传统复杂的电离层模型,在已知一个参考站接收机硬件延迟的条件下,利用正常情况下电离层延迟量和卫星-接收机几何距离强相关这一特点,采用站间单差法来精确估计区域内BDS接收机的硬件延迟。试验结果表明,该方法单站估计的单站北斗接收机连续30d的硬件延迟RMS在0.3ns左右。通过GEO卫星双频观测值扣除已知卫星DCB和本文方法估计的接收机DCB,计算对应穿刺点一天的VTEC并和GIM格网内插结果并进行比对分析,二者大小和变化趋势均符合较好,进一步验证了本文提出的方法具有可靠性。     

Swarm低轨卫星星座的GPS接收机差分码偏差估计

差分码偏差(differential code bias,DCB)是指由全球导航卫星系统(global navigation satellite system, GNSS)信号接收和发射硬件导致的频率相关的偏差项,对电离层估计有显著的影响,在利用GNSS观测数据提取电离层总电子含量时需要被精确修正,研究利用低轨卫星的星载GNSS观测数据估计DCB尤为重要。使用Swarm星座3颗卫星GPS接收机2016年1月的双频观测值,设计了独立估计和联合估计两种估计方案,采用附加限制条件的间接平差方法对GPS卫星以及星载接收机的DCB进行估计。以中国科学院和德国宇航中心的DCB产品作为参考,分析了两种估计方案的精度和稳定性,相较于独立估计方案,联合估计方案得到的GPS卫星DCB的稳定性较独立估计方案提高了16.6%,且与参考DCB具有更好的一致性。     

M_DCB: Matlab code for estimating GNSS satellite and receiver differential code biases

Global navigation satellite systems (GNSS) have been widely used to monitor variations in the earth’s ionosphere by estimating total electron content (TEC) using dual-frequency observations. Differential code biases (DCBs) are one of the important error sources in estimating precise TEC from GNSS data. The International GNSS Service (IGS) Analysis Centers have routinely provided DCB estimates for GNSS satellites and IGS ground receivers, but the DCBs for regional and local network receivers are not provided. Furthermore, the DCB values of GNSS satellites or receivers are assumed to be constant over 1?day or 1?month, which is not always the case. We describe Matlab code to estimate GNSS satellite and receiver DCBs for time intervals from hours to days; the software is called M_DCB. The DCBs of GNSS satellites and ground receivers are tested and evaluated using data from the IGS GNSS network. The estimates from M_DCB show good agreement with the IGS Analysis Centers with a mean difference of less than 0.7?ns and an RMS of less than 0.4?ns, even for a single station DCB estimate.     

Revisit the calibration errors on experimental slant total electron content (TEC) determined with GPS

The calibration errors on experimental slant total electron content (TEC) determined with global positioning system (GPS) observations is revisited. Instead of the analysis of the calibration errors on the carrier phase leveled to code ionospheric observable, we focus on the accuracy analysis of the undifferenced ambiguity-fixed carrier phase ionospheric observable determined from a global distribution of permanent receivers. The results achieved are: (1) using data from an entire month within the last solar cycle maximum, the undifferenced ambiguity-fixed carrier phase ionospheric observable is found to be over one order of magnitude more accurate than the carrier phase leveled to code ionospheric observable and the raw code ionospheric observable. The observation error of the undifferenced ambiguity-fixed carrier phase ionospheric observable ranges from 0.05 to 0.11 total electron content unit (TECU) while that of the carrier phase leveled to code and the raw code ionospheric observable is from 0.65 to 1.65 and 3.14 to 7.48 TECU, respectively. (2) The time-varying receiver differential code bias (DCB), which presents clear day boundary discontinuity and intra-day variability pattern, contributes the most part of the observation error. This contribution is assessed by the short-term stability of the between-receiver DCB, which ranges from 0.06 to 0.17 TECU in a single day. (3) The remaining part of the observation errors presents a sidereal time cycle pattern, indicating the effects of the multipath. Further, the magnitude of the remaining part implies that the code multipath effects are much reduced. (4) The intra-day variation of the between-receiver DCB of the collocated stations suggests that estimating DCBs as a daily constant can have a mis-modeling error of at least several tenths of 1 TECU.     

Is the long-term variation of the estimated GPS differential code biases associated with ionospheric variability?

The global positioning system (GPS) differential code biases (DCB) provided by the International GNSS Service (IGS) show solar-cycle-like variation during 2002–2013. This study is to examine whether this variation of the GPS DCBs is associated with ionospheric variability. The GPS observations from low earth orbit (LEO) satellites including CHAMP, GRACE and Jason-1 are used to address this issue. The GPS DCBs estimated from the LEO-based observations at different orbit altitudes show a similar tendency as the IGS DCBs. However, this solar-cycle-like dependency is eliminated when the DCBs of 13 continuously operating GPS satellites are constrained to zero-mean. Our results thus revealed that ionospheric variation is not responsible for the long-term variation of the GPS DCBs. Instead, it is attributed to the GPS satellite replacement with different satellite types and the zero-mean condition imposed on all satellite DCBs.