GPS and BeiDou Differential Code Bias Estimation Using Fengyun-3C Satellite Onboard GNSS Observations

Li, Wenwen; Li, Min; Shi, Chuang; Fang, Rongxin; Zhao, Qile; Meng, Xiang‐Gao; Yang, Guanglin; Bai, Weihua

doi:10.3390/rs9121239

Cited by 18 publications

(6 citation statements)

References 37 publications

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“…In recent years, scholars have studied and used LEO onboard observation data to estimate DCB parameters. A few scholars estimated the DCBs of the GPS satellite and LEO receiver as unknown parameters simultaneously [16][17][18], and others [19][20][21][22][23][24][25] not only estimated the DCB parameters of GPS and LEO satellites but also estimated the corresponding VTEC parameters. Certain scholars introduced external GPS DCB products and estimated only the corresponding topside ionosphere VTEC and LEO receiver DCB parameters [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Effects of Topside Ionosphere Modeling Parameters on Differential Code Bias (DCB) Estimation Using LEO Satellite Observations

Wang,

Liu,

Yuan

et al. 2023

Remote Sensing

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Given the potential of low-earth orbit (LEO) satellites in terms of navigation enhancement, accurately estimating the differential code bias (DCB) of GNSS satellites and LEO satellites is an important research topic. In this study, to obtain accurate DCB estimates, the effects of vertical total electron content (VTEC) modeling parameters of the topside ionosphere on DCB estimation were investigated using LEO observations for the first time. Different modeling parameters were set in the DCB estimations, encompassing modeling spacing in the dynamic temporal mode and degree and order (D&O) in spherical harmonic modeling. The DCB precisions were then evaluated, and the impacts were analyzed. Thus, a number of crucial and beneficial conclusions are drawn: (1) The maximum differences in the GPS DCB estimates after adopting different modeling spacings and different D&Os exhibit that the different modeling spacings or D&Os both affect the GPS DCB estimates and their root-mean square (RMS), and the effects of the two are at the same level. (2) The maximum differences in receiver DCBs using different modeling spacings indicate that the modeling spacing has a significant impact on the receiver DCBs, compared with GPS DCBs. Whereas, the maximum differences in receiver DCBs with different modeling D&Os are inferior to the differences in the GPS DCBs. That is, the modeling spacing has a greater impact on the LEO DCBs than those of the modeling D&O. (3) The experimental results indicate that the GPS DCB estimates using a modeling spacing of 12H have higher precisions than the others, whereas LEO receiver DCBs using a spacing of 4H or 6H obtain optimal STD. In terms of modeling D&O, adopting 8D&O in the LEO-based VTEC modeling can attain superior estimates and precisions for both GPS and LEO DCBs. The research conclusions can provide references for LEO-augmented DCB estimation.

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Section: Introductionmentioning

confidence: 99%

Effects of Topside Ionosphere Modeling Parameters on Differential Code Bias (DCB) Estimation Using LEO Satellite Observations

Wang,

Liu,

Yuan

et al. 2023

Remote Sensing

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“…Compared to a relative form of bias using DCB corrections, the pseudo-absolute code biases provided in observable-specific signal bias (OSB) products is an approach towards a flexible, expandable handling of GNSS bias [11,12]. In addition to temporal scale variation of SCBs, Li et al [13] estimate GNSS DCBs with the onboard receiver of the China Fengyun-3C satellite. The results indicate that the onboard receiver DCB is very stable.…”

Section: Introductionmentioning

confidence: 99%

Variation of receiver code biases under the influence of the receiver type and antenna configuration in the IGS network

Li¹,

Zhang²,

Yuan³

2022

Meas. Sci. Technol.

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Receiver code biases (RCBs) are known to be time delays within the receiver caused by their hardware imperfections. To better understand the characteristics of RCBs, the un-combined (UC) and ionosphere-free (IF) precise point positioning functional models are adapted and re-parameterized to estimate the variation of RCBs as a time-variant parameter. In this study, we analytically studied the temporal variations of RCBs; although there exists a benchmark difference between the UC and IF models, their estimates are in accordance with each other. Additionally, this contribution assesses the inter-day stability of RCBs with weekly observations from 165 globally distributed international global navigation satellite system service stations equipped the receivers of three mainly types. The inter-day stability results of RCB revealed that the RCBs of POL2 and OUS2 have better stability over consecutive 7 d and the single differenced (SD) RCBs can reach 0.2 m in the best case. The results show that 74.83% of the stations are equipped with Trimble receivers under the condition that the mean SD RCB values are between −0.5 and 0.5 m, while 85.57% of the stations are equipped with Septentrio receivers and the stations equipped with Javad can reach 84.35% under this condition. The RCB estimates are also relatively stable for the case in which the receiver hardware device stays unchanged. The relationship between RCBs, receiver type, and antenna configuration is found using six groups of receivers. A strong correlation exists between RCBs, receiver type, and antenna configuration, which is more obvious among Septentrio receivers. The results show that the Pearson correlation coefficients were all higher than 0.9, and the standard deviation of between-receiver RCBs was smaller than 0.327 m when equipped with Septentrio receivers. We concluded that there is a strong relationship between the receiver-related pseudorange biases and the receiver and antenna setup.

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“…The first one is to estimate the DCBs with a global or local ionosphere model simultaneously [9,10]. Another one is to estimate the DCBs after eliminating the ionosphere delays with a prior ionosphere model (e.g., German Aerospace Center (DLR)) [11,12]. The prior ionosphere models generally include the global ionospheric map (GIM) and some broadcast ionospheric models [13].…”

Section: Introductionmentioning

confidence: 99%

Estimation and analysis of BDS-3 multi-frequency differential code bias using MGEX observations

Yuan,

Hu,

et al. 2022

JGPS

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Differential code bias (DCB) significantly affects the ionosphere modeling, precise positioning, and navigation applications when using code observations. With the fully completed BeiDou navigation satellite system (BDS-3), there exist various DCBs of new frequencies and types which should be handled. However, limited types of DCB products for BDS-3 are provided by the analysis institutions (e.g., Chinese Academy of Science (CAS) and German Aerospace Center (DLR)). Hence, for some DCB corrections of new frequencies, they are generally generated by complex linear combinations, which are not friendly to users and may degrade the accuracy. In this study, the estimation method of DCB for BDS-3 is introduced first. Then, the BDS-3 observations from 40 globally distributed stations are selected to estimate the DCBs, including 19 types of DCBs of new frequencies for BDS-3. Moreover, the estimated DCBs are carefully analyzed in terms of inner consistency, external consistency, and stability. For the results of inner consistency, most closure error series are within 0.2 ns, and the closure error series of each satellite fluctuate near zero and have no obvious systematic deviations. For the results of external consistency, the mean deviations of estimated DCBs of each satellite are mainly within 0.3 ns and 0.2 ns for the common types of DCB products of CAS and DLR, respectively. For the results of stability, the mean values of monthly STDs for the estimated DCBs are all smaller than 0.12 ns, which exhibits good stability. The STDs of the directly estimated DCBs are generally smaller than that of the DCB combinations of DLR and CAS. In this sense, the directly estimated DCBs for BDS-3 exhibits good performance in terms of accuracy and stability in this study, which can further provide the DCB corrections for precise positioning and navigation applications.

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GPS and BeiDou Differential Code Bias Estimation Using Fengyun-3C Satellite Onboard GNSS Observations

Cited by 18 publications

References 37 publications

Effects of Topside Ionosphere Modeling Parameters on Differential Code Bias (DCB) Estimation Using LEO Satellite Observations

Effects of Topside Ionosphere Modeling Parameters on Differential Code Bias (DCB) Estimation Using LEO Satellite Observations

Variation of receiver code biases under the influence of the receiver type and antenna configuration in the IGS network

Estimation and analysis of BDS-3 multi-frequency differential code bias using MGEX observations

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