GPS Solutions

Seasonal signals in satellite geodesy time series are mainly derived from a number of loading sources, such as atmospheric pressure and hydrological loading. The most common method for modeling the seasonal signal with quasi-period is to use the sine and cosine functions with the constant amplitude for approximation. However, due to the complexity of environmental changes, the time-varying period part is very difficult to model by the geometric or physical method. We present a machine learning method with Gaussian process to capture the quasi-periodic signals in the geodetic time series and optimize the estimation of model parameters by means of maximum likelihood estimation. We test the performance of the method using the synthetic time series by simulating the time-varying and quasi-periodic signals. The results show that the fitting residuals of the new model show a better random fluctuation, while the traditional models still leave the clear periodic systematics signals without being fully modeled. The new model illustrates a higher reliability of linear trend estimation, and a lower uncertainty and model fitting RMSE, even in time series with shorter time span. On the other hand,  it shows a strong capacity to restore the missing data and predict the future changes in time series. The method is successfully applied to modeling the real coordinate time series of the GNSS site (BJFS) from IGS network, and the equivalent water height (EWH) time series in North China obtained from gravity satellites. Therefore,  it is recommended as an alternative for precise model reconstruction and signals extraction of satellite geodesy time series, especially in modeling the complex time-varying signals, estimating the secular motion velocity, and recovering the large missing data.

The International Reference Ionosphere (IRI) model is a widely used empirical model to describe ionospheric climatology. However, IRI represents the monthly averages of the ionospheric parameters, which makes it difficult to capture the local and short-term ionospheric variations. To overcome this limitation, we propose a data ingestion method using a combination of ground-based and space-borne observations. The ionospheric parameters from ground-based Global Navigation Satellite System (GNSS), ionosondes, space-borne GNSS radio occultation and satellite altimetry observations are ingested into the IRI-2020 model to improve its accuracy. The outputs of the ingested IRI (IRIinge) are assessed by case study and statistical analysis, with reference to independent ionosonde observations and global ionospheric maps. The case study shows that IRIinge expresses the diurnal and local variations of the ionosphere better than the standard IRI (IRIstan) in both high and low solar activity periods. The relative error of ionospheric electron density profiles from IRIinge is generally less than 10%, and the vertical total electron content from IRIinge has an accuracy improvement of 39.0% compared to that from IRIstan. The statistical analysis shows that IRIinge performs more stable than IRIstan, and its output generally has smaller REs and root-mean-square errors, especially in daytime and storm time. The proposed method significantly improves IRI-2020 on the accuracy of the output parameters and the ability to present the short-term variations of the ionosphere.

The massive number of global navigation satellite system (GNSS) users and frequent positioning demands in cities, as well as the complexity of urban scenarios, pose many challenges for the accuracy and reliability of precise positioning. Since urban environments tend to suffer from GNSS non-line-of-sight (NLOS) signal conditions, leading to large ranging errors, NLOS signal identification and processing are of great importance. Usually, a visual camera can reflect real occlusion, and machine learning is efficient and accurate in processing multiple types of features. Therefore, an algorithm is proposed that combines the advantages of both methods. First, NLOS labels are generated using a combination of an inertial navigation system (INS) and a fisheye camera, and a total of nine features, namely, the elevation angle as well as the signal-to-noise ratios (SNRs), SNR fluctuation magnitudes, pseudorange consistencies, and pseudorange multipath errors at two frequencies, are extracted. Then, to improve efficiency and avoid overfitting, the nine original features are aggregated into three common factors via factor analysis, and these three factors can be well interpreted. Finally, a NLOS signal identification model based on the random forest (RF) algorithm is designed. In addition, to improve the precise point positioning (PPP) performance, a weighting scheme based on the elevation angle and SNR is optimized in accordance with the probability of NLOS occurrence. In an experiment, the RF model is trained using on-board dynamic multi-GNSS dual-frequency data collected by a low-cost UBLOX F9P receiver in Wuhan, and then validation is performed using data collected in Wuhan and Zhengzhou. The experimental results show that compared with the gradient boosted decision tree (GBDT), support vector machine (SVM), naive Bayes (NB), and convolutional neural network (CNN) algorithms, the RF model shows superior performance. While achieving 87.5% and 72.5% accuracy on the local and remote test datasets, respectively, the RF model costs only 12.2 ms for LOS/NLOS classification per epoch. Moreover, through factor analysis, the computational efficiency is improved by 29.5% for all five algorithms. Additionally, the accuracy and stability of uncombined PPP are improved using the proposed weighting strategy.

Dual-frequency multi-constellation (DFMC) satellite-based augmentation system (SBAS) is a new SBAS standard for aeronautical navigation systems. It supports aircraft navigation from the enroute to approach phases via the L1 and L5 frequencies (1575.42 and 1176.45 MHz). Although the ionosphere-free (IF) combination in the DFMC SBAS operation removes the first-order ionospheric delays in the pseudorange measurement, remaining terms including the satellite-clock offset errors and higher-order ionospheric (HOI) delays are still unaccounted for. The DFMC SBAS accuracy and integrity can be affected by the HOI effects, especially during severe ionospheric disturbances. In this work, we present the local DFMC SBAS corrections with and without the mitigation of HOI delays. We first estimate the HOI delay terms using the received pseudorange followed by separate satellite and receiver bias estimations based on the minimum sum-variance technique. The integrity terms can then be obtained. The performances of DFMC SBAS using the global navigation satellite system (GNSS) data including GPS, Galileo, and QZSS are evaluated using obtained GNSS data at stations in Thailand on the ionospheric quiet and disturbed days. The results show that with the HOI mitigation, the vertical positioning errors (VPE) on the quiet and disturbed days can be improved by 12% and 9%, whereas the vertical protection levels (VPL) are improved by 16% and 21%, respectively. In addition, we perform a preliminary assessment of DFMC SBAS based on the International Civil Aviation Organization (ICAO) requirements of two categories: Localizer Performance with Vertical guidance (LPV-200) and Category I precision approach (CAT-I) showing promising results.

In land surface remote sensing using Global Navigation Satellite System reflectometry (GNSS-R) signals, it is common to observe signal coupling between the reflections from the soil surface and vegetation. But most recent research focuses on either bare soil or single vegetation. The vegetation significantly reduces the amplitude of the GNSS signal and increases the standard deviation (STD) of the carrier phase and pseudorange calculations. This study proposes a solution that uses GNSS transmission reflectometry (GNSS-TR) and wavelet transform to decouple signals reflected off vegetation-covered and bare soil surfaces. This coupling persists despite the ability of wavelet transform to initially separate the signal-to-noise ratio (SNR) sequences of GNSS reflected signals into different frequency components. This study uses the power of GNSS transmission signals, which carry almost exclusively vegetation information, as a priori information input to calibrate the influence of vegetation on the reflected signal from the soil surface to maximize the decoupling of the two reflected signals. Furthermore, signals from above- and below-vegetation GNSS antennas were simultaneously collected using a low-cost, dual-channel GNSS chipset, which can increase GNSS signal processing channels while reducing equipment costs. The results show that the solution proposed in this study can reach a correlation coefficient of 0.96 between the retrieval and in situ soil moisture content (SMC), and the root mean square error (RMSE) is reduced to 0.013 cm3/cm3. Moreover, the transmitted signal power, pseudorange STD, and carrier phase STD showed a clear trend with the vegetation growth status (VGS).

The atmospheric weighted mean temperature (Tm) is a key parameter in determining the precipitable water vapor (PWV). Conventional meteorological parameter empirical models have a lower spatial resolution and poor regional applicability, resulting in lower accuracy in obtaining the Tm values in global navigation satellite system (GNSS) PWV retrieval. We discuss a long short-term memory-based ERA5 temperature (LSTM-ERATM) model and evaluate the accuracy of calculating the Tm. Considering Tm’s annual, semi-annual, and daily cycle characteristics, an ERATM model was developed based on the ERA5 data from 2017 to 2020 provided by the European Center for Mesoscale Weather Forecasts (ECMWF). Then, the LSTM model was used to train the differences between the Tm values obtained by discrete integration of the ERA5 data and Tm values calculated by the ERATM model to enhance the accuracy of the ERATM model. We use the ERA5 and sounding data from 2021 to 2022 to analyze the calculation effect of the LSTM-ERATM, ERATM, GPT3, UNB3, and Bevis models. The results show that the ERATM model has broad regional applicability and can provide high-accuracy Tm. Compared with the UNB3, GPT3, and Bevis models, the mean root-mean-square (RMS) values of the ERATM model is reduced by 43.4%, 3.4%, and 11.7% respectively when using the ERA5 data as the reference values, and reduced by 22.9%, 13.9%, and 0.2% respectively when using the sounding data as the reference values. Moreover, the accuracy of the LSTM-ERATM is generally better than ERATM at different time points and regions, which shows that the LSTM model effectively improves the accuracy of the ERATM model in calculating Tm. For example, the mean RMS values of LSTM-ERATM were reduced by 50.8%, 37.4%, 26.2%, and 18.9% in the next time points of 6:00, 12:00, 18:00, and 24:00 respectively when using the ERA5 data as the reference values, and reduced by 31.3%, 27.2%, 35.9%, and 8.6% respectively when using the sounding data as the reference values. The LSTM-ERATM model in this study provides a powerful tool to improve the accuracy of calculating Tm, which can provide more reliable data for meteorology and climate research.

High-precision satellite laser ranging measurements to Galileo retroreflector panels are analyzed to determine the angle of incidence of the laser beam based on specific orientations of the panel with respect to the observing station. During the measurements, the panel aligns with respect to the observing station in such a way that multiple retroreflectors appear at the same range, forming regions of increased data density—separated by a few millimeters. First, measurements to a spare IOV-type retroreflector mounted on an astronomical mount at a remote location 32 km away from the Graz laser ranging station are performed. In addition, more than 100 symmetry passes to Galileo satellites in orbit have been measured. Two novel techniques are described to form laser ranging normal points with improved precision compared to traditional methods. An individual normal point can be formed for each set of retroreflectors at a constant range. The central normal point was shown to be up to 4 mm more accurate when compared with a precise orbit solution. Similar offsets are determined by applying a pattern correlation technique comparing simulated with measured data, and the first method is verified. Irregular reflection patterns of Galileo FOC panels indicate accumulated far-field diffraction patterns resulting from non-uniform retroreflector distributions.

Orbit prediction (OP) recently tends to be a very crucial step for supporting real-time GNSS orbit services due to the dynamic stability of navigation satellite orbits. The OP performance depends on the length of the predicted orbits and the accuracy of precise orbit determination (POD) as basis. Considering this, a new automatic processing engine is established for improving the multiple global navigation satellite systems (multi-GNSS) constellation OP performance. From the architecture-oriented high-performance parallel processing perspective, the multi-node and multi-core computer sources are fully exploited to implement the hourly update of the current multi-GNSS POD. For MEO-type satellites (e.g., Galileo satellites), the accuracy of predicted orbits is improved from 3.8 cm, 6.5 cm, and 12.3 cm to 3.5 cm, 4.3 cm, and 6.3 cm, in the radial, cross, and along directions, respectively, compared to the three-hour POD update. Despite the shortened OP length, the OP performance of regional navigation satellite system (RNSS) satellites is still limited due to their regional observability. The BDS-IGSO and QZSS-IGSO satellitesexhibit radial directional orbital errors of up to 36.9 cm and 28.9 cm, respectively. Therefore, an orbit fitting (OF) processing method with orbit reconstruction is implemented into the processing engine. By utilizing this method, the radial orbital errors for BDS-IGSO and QZSS-IGSO satellites can be reduced to 7.0 cm and 10.4 cm, respectively. The mean real-time positioning errors are thus reduced from 28.3 to 18.4 cm and from 24.4 to 18.2 cm in the horizontal and vertical components, respectively.

The Galileo high accuracy service (HAS) is a free-of-charge service designed to deliver decimeter-level accuracy in real-time precise point positioning (PPP) applications using global navigation satellite systems (GNSS). With the intention of facilitating the use of HAS corrections with open access tools, we present the open-source library named HASlib and its integration with another open-source library named RTKLIB. HASlib decodes the Reed–Solomon encoded Galileo E6 navigation data pages and outputs the corrections in commonly used formats. This enables the utilization of HAS with conventional GNSS receivers and PPP engines that lack native support for HAS formats. For instance, the outputs from HASlib enable the use of HAS corrections in RTKLIB. In order to validate this integration, we demonstrate that HAS can allow decimeter-level accuracy using only free-of-charge services and tools. We have obtained a 3D root mean square error below 20 cm (1 sigma) after a convergence time of 10–90 min in Finland. This accuracy has overcome classical real-time solutions with broadcast and satellite-based augmentation system (SBAS) data by one order of magnitude. Compared to post-processed multi-GNSS PPP, HAS corrections required longer convergence times, given the real-time nature. Furthermore, our assessment revealed that the longer convergence time, compared to prior literature, was attributed to RTKLIB filtering procedure and geometry deficiencies in high latitudes. Nevertheless, once convergence was attained, a sub-decimeter level of accuracy was observed in both horizontal and vertical components. These findings highlight the effectiveness of Galileo HAS, HASlib, and RTKLIB as powerful tools for providing open-access to real-time PPP solutions.

The International GNSS Service (IGS) provides combined satellite and station clock products, which are generated from the individual clock solutions produced by the analysis centers (ACs). Combinations for GPS and GLONASS are currently available, but there is still a lack of combined products for the new constellations such as Galileo, BeiDou, and QZSS. This study presents a combination framework based on least squares variance component estimation using the ACs’ aligned clock solutions. We present the various alignments required to harmonize the solutions from the ACs, namely the radial correction derived from the differences of the associated orbits, the alignment of the AC clocks to compensate for different reference clocks within each AC solution, and the inter-system bias (ISB) alignment to correct for different AC ISB definitions when multiple constellations are used. The combination scheme is tested with IGS MGEX and repro3 products. The RMS computed between the combined product and the aligned ACs’ solutions differ for each constellation, where the lowest values are obtained for Galileo and GPS with on average below 45 psec (13 mm) and reaching more than 150 psec (45 mm) for QZSS. The same behavior is repeated when the process is performed with the repro3 products. A clock and orbit combination validation is done using precise point positioning (PPP) that shows ionosphere-free phase residuals below 10 mm for all constellations, comparable with the AC solutions that are in the same level.

Ionospheric indices give information about ionospheric perturbations, which may cause absorption, diffraction, refraction, and scattering of radio signals, including those from global navigation satellite systems (GNSS). Therefore, there may be a relationship between index values and GNSS positioning results. A thorough understanding of ionospheric indices and their relationship to positioning results can help monitor and forecast the reliability and accuracy of GNSS positioning and support the precision and safety of life applications. In this study, we present the relationship between three indices: Gradient Ionosphere indeX (GIX), Sudden Ionospheric Disturbance indeX (SIDX), and Rate of Total electron content Index (ROTI) in relation to precise positioning results. We used two approaches: precise point positioning (PPP) and relative positioning for long baselines. We focus on GNSS stations located in Europe for two selected geomagnetic storms: March 17, 2015, and May 22, 2015. Our results show that in the case of PPP, positioning degradation occurred mainly at high latitudes and was mostly caused by rapid small-scale changes in ionospheric electron content represented by SIDX and ROTI. We also showed a significant correlation between cycle slips of GNSS signals and ROTI (0.88). The most significant degradations for relative positioning for low and medium latitudes were associated with large spatial gradients reflected by the GIX.

The precise point positioning real-time kinematic (PPP-RTK) is a high-precision global navigation satellite system (GNSS) positioning technique that combines the advantages of wide-area coverage in precise point positioning (PPP) and of rapid convergence in real-time kinematic (RTK). However, the PPP-RTK convergence is still limited by the precision of slant ionospheric delays and tropospheric zenith wet delay (ZWD), which affects the PPP-RTK network parameters estimation and user positioning performance. The present study aims to construct a PPP-RTK model augmented with a priori ZWD values derived from the global forecast system (GFS) product (a global numerical weather prediction (NWP) model) to improve the PPP-RTK performance. This study gives a priori ZWD values and conversion based on the GFS products, and the full-rank GFS-augmented undifferenced and uncombined (UDUC) PPP-RTK network model is derived. To verify the performance of GFS-augmented UDUC PPP-RTK, a comprehensive evaluation using 10-day GNSS observation data from three different GNSS station networks in the United States (US), Australia, and Europe is conducted. The results show that with the GFS ZWD a priori information, PPP-RTK performance significantly improves at the initial filtering stage, but this advantage gradually decays over time. Based on 10-day positioning results for all user stations, the GFS ZWD-augmented PPP-RTK approach reduces the average convergence time by 46% from 10.0 to 5.4 min, the three-dimensional root-mean-square (3D-RMS) error by 5.7% from 3.5 to 3.3 cm, and the time to first fix (TTFF) value by 35.8% from 6.7 to 4.3 min, all when compared to the traditional PPP-RTK without GFS ZWD constraints.

While few phase bias streams are available from the IGS Real-time Service Phase, such products are essential to enable PPP ambiguity resolution. Satellite phase biases and clocks should be estimated when fixing undifferenced ambiguities in a network solution, which is troublesome in real time and thus usually not done in practice. This study estimates real-time GPS/Galileo/BDS ambiguity-fixed multi-frequency raw phase biases from GNSS observation modeling. Multi-frequency narrow-lane and wide-lane uncalibrated phase delays (UPDs) are first extracted from the float ambiguities of the stations. Multi-frequency raw ambiguities are then resolved to form unambiguous carrier-range observations. Finally, the satellite phase observable-specific signal biases (phase OSBs) are estimated directly from the real-time network data processing using carrier-range observations only. The approach has been applied to generate real-time phase bias products at the Wuhan IGS analysis center. Real-time products are routinely calculated by 180 globally distributed stations. This study validates the approach and associated products using one week of data. The results show that triple-frequency kinematic PPP-AR based on the ambiguity-fixed phase OSBs can converge in 7.2 min on average, while those based on UPD products take 11.2 min to converge. The other software, PRIDE PPP-AR is used to validate the high-precision static positioning performance of the real-time OSB products. The results show that 82%, 85% and 76% of GPS, Galileo and BDS-3 narrow-lane ambiguities can be resolved successfully among global stations, achieving a mean positioning accuracy of 3.1, 3.0 and 6.0 mm for the east, north and up components.

This study aimed to investigate the performance of machine learning (ML) algorithms in determining horizontal velocity at specific points using the current Global Navigation Satellite System (GNSS) velocity field. To achieve this objective, the analysis utilized the most comprehensive velocity field available for Turkey, where 70% of the GNSS velocities was allocated for training the ML algorithms, while the remaining 30% was used for testing. Contrary to the previous research, the significance of considering the tectonic structure within the study area was emphasized at this point. To determine the tectonic structure of the horizontal velocity field in the region, a preliminary clustering procedure was conducted. Subsequently, distinct ML algorithms were trained using velocity fields associated with different tectonic plates. Moreover, to investigate the impact of the tectonic domain, the entire velocity field was also tested using ML algorithms without considering the tectonic structure. Four different ML algorithms, namely, Gradient Boosting Machines (GBM), LightGBM, Random Forest (RF), and eXtreme Gradient Boosting Machines (XGBoost), were employed to estimate the horizontal velocities (east and north components). The findings imply that incorporating the tectonic structure improved the performance of machine learning predictions, as indicated by the GBM algorithm's decreased root-mean-square error values. In addition, when the tectonic structure was taken into account, the accuracy assessment values for the RF and XGBoost algorithms in the east component decreased significantly. In terms of predicting GNSS velocities, the RF algorithm exhibited the lowest root-mean-square error values compared to other algorithms. The horizontal velocity differences between averages of the reference velocity field and the RF velocity estimates are maximum 0.4 mm/yr.

As an emerging observational method, spaceborne global navigation satellite system-reflectometry (GNSS-R) has been applied recently for significant wave height (SWH) retrieval. However, the complexity of the sea surface and the influence of multiple potential factors have been constraining the accuracy of SWH retrieval. This study verified the effect of sea surface temperature (SST), sea surface salinity (SSS), and seasonal variation on cyclone-GNSS (CYGNSS) observables for the first time. After controlling for the SWH, the CYGNSS observables exhibit a dependence on SST and SSS, where the dependence on SST dominates. The correlation coefficient (R) between SST and CYGNSS observables is the highest in 3.5–4 m, which is 0.53. In addition, the geographical distribution of retrieval bias exhibits seasonality. Therefore, seasonal factors can provide an additional contribution to SWH retrieval. SWH retrieval is based on the multilayer perceptron. The European center for medium-range weather forecast reanalysis 5th Generation SWH data were used as the reference for the computation of retrieval performance metrics. The results show that after considering SST, salinity, and season, the root mean square error (RMSE) of the retrieved SWH decreases from 0.65 to 0.48 m and the R increases from 0.66 to 0.83. The retrievals were compared to the ground truth measurements from the National Data Buoy Center buoys; the RMSE decreased from 0.52–1.07 m to 0.30–0.61 m, and the R increased from 0.44–0.71 to 0.60–0.78.

Precise information about the phase center corrections (PCCs) for BeiDou satellite antennas is not only essential to the generation of high-precision orbit and clock products but also important to the determination of terrestrial scale. However, the existing BDS PCCs still suffer from some deficiencies, such as the lack of phase center variations (PCVs) for several BDS satellites as well as the misalignment of phase center offsets (PCOs) and the latest IGS20 frame. We present the estimation results of both PCVs and PCOs for all 37 currently in-service BDS Inclined Geosynchronous Satellite Orbit (IGSO) and Medium Earth Orbit (MEO) satellites. The results demonstrate that the nadir-angle-dependent PCVs of the same satellite block type have fairly good consistency. The horizontal PCO estimates and the igs20.atx values have a consistency of several centimeters. As for the Z-PCOs aligned to IGS20 frame, the estimated values are 2380 and 530 mm smaller than the igs20.atx values for BDS-2 IGSO and BDS-2 MEO satellites, while those of BDS-3 MEOs have mean differences of −61 and 46 mm with respect to igs20.atx values for satellites manufactured by the China Academy of Space Technology and Shanghai Engineering Center for Microsatellites, respectively. By using the estimated PCCs instead of the igs20.atx values, the precise orbit and clock determination precision can be improved by 5–32% and 4–20%, respectively. Furthermore, applying the estimated PCCs, the scale factors determined by BDS solutions exhibit good consistency with IGS20 frame, with mean scale differences below 0.15 ppb.

Amplitude scintillations in Global Navigation Satellite System (GNSS) signals are commonly observed at low latitudes and are frequently associated with equatorial plasma bubbles. The scintillation severity is enhanced around the equatorial ionization anomaly, being controlled, in great part, by the ionospheric F-region background density. This work proposes the use of collocated observations from space-based and distributed ground-based monitors to quantify the relationship between the background F-region peak electron density (NmF2) and scintillation severity. To test the proposed approach and its feasibility, NmF2 observations from the Global-scale Observations of the Limb and Disk (GOLD) instrument and L-band scintillation measurements made by a network of GNSS-based scintillation monitors were used. The observations were made at low latitudes in October 2022, during the ascending phase of solar cycle 25. Results show the influence of background NmF2 on scintillation severity. The results also quantify the control of the latitudinal distribution of maximum S4 values [S4 (max)] by the latitudinal variation of NmF2. An empirical relationship between NmF2 and S4 (max) for a given local time was also derived for the time of GOLD observations. An application of the empirical relationship between NmF2 and maximum S4 is illustrated with regional (Brazilian) maps of potential maximum scintillation severity using GOLD-like data. Encouraging results include showing that S4 (max) can be estimated from independent observations for a distinct longitude sector, but similar solar flux and season. Future studies will address to what extent the relationship between NmF2 and S4 (max) varies for different geophysical conditions.

Intelligent vehicles usually equip with GNSS receivers and MEMS-IMU for localization and light detection and ranging (LiDAR) sensors for perception. Cycle slip detection and repair is significant for the GNSS receivers to achieve high-precision positioning results. We propose an improved geometry-based cycle slip detection and repair method considering the positioning error at the immediate prior epoch and the influence of the satellite geometry change. Experimental results show that our improved method can improve fixed rates of ambiguity resolution compared to the traditional geometry-based method, especially for long-term satellite signal loss. Based on the improved method, we further propose a LiDAR–INS aiding LAMBDA method for cycle slip detection and repair. The INS model and LiDAR scan-to-map matching results are fused by extended Kalman filter (EKF) to calculate between-epoch relative position, which provides a constraint for LAMBDA. Experimental results prove that our LiDAR–INS positioning method can get centimeter-level accuracy for small observation gaps (e.g., 10 s) and decimeter-level accuracy for large observation gaps (e.g., 1 min), which can help achieve high fixed rates and success rates (e.g., 0.85) for large observation gaps (e.g., 200 s) in the urban occlusion environment where prior epoch owns meter-level positioning accuracy.

The high-precision time and frequency transfer method, based on the global navigation satellite system (GNSS) precise point positioning (PPP), has high precision, wide range, and low cost, and the GNSS common-view (CV) can remove all satellite clock errors and partial satellite orbit errors. We conducted a study to select the best satellite and combined PPP and CV to eliminate satellite clock errors, weaken the impact of satellite orbit errors, and unmodeled atmospheric asymmetry in PPP frequency transfer, thus improving the performance of frequency transfer. This study uses conventional dual-frequency ionosphere-free PPP that does not solve for an azimuthal asymmetry in the troposphere. It uses international GNSS service (IGS) products to determine the carrier phase difference between each CV satellite time and the ground clock, for each epoch. Then, the comparison difference is obtained by directly subtracting the carrier phase differences between the CV satellite time and two ground clocks. For each hour, only the satellite that is fully visible at both sites and gives the smallest standard deviation in time comparison of the day between the two ground clocks is selected as the CV satellite. To evaluate the performance of PPP-CV, five stations connected to individual active hydrogen masers are selected to form four links, of which two stations (USN7 and USN8) are common-clock and common-antenna. The results show that the time comparison precision of PPP-CV improves by approximately 12% on average for the three European links compared to PPP with respect to IGS final clock products. For frequency transfer modified Allan deviation (MDEV) over 600,000 s, PPP-CV can reach 2 × 10–16 and 2 × 10–17 for the SPT0-IENG and USN7-USN8 links, respectively. In addition, the frequency transfer stability ranging from 1200 s to 60,000 s of PPP-CV improves by 7% on average compared to PPP, and its short-term stability is also better than that of PPP when the CV satellite does not change. However, the performance of PPP-CV is comparable to PPP when the link length reaches 5991 km and the short-term stability of PPP-CV is slightly worse than PPP when the CV satellite is constantly changing.