GPS Solutions

Abstract

To meet the high-precision and high-integrity positioning demands of safety–critical applications, monitoring the quality of precise satellite products in global navigation satellite system (GNSS) precise point positioning (PPP) is crucial. This work employs ionosphere-free (IF) PPP with ambiguity resolution (PPP-AR) phase residuals to construct test statistics for monitoring the quality of precise satellite corrections. By utilizing precise satellite orbit and clock products from CODE, WUM, and GRG, the PPP-AR phase residuals were first analyzed with sample moments, Allan variance and power spectral density (PSD). The key findings are as follows: (1) The skewness and kurtosis results indicate that ambiguity-fixed phase residuals deviate from an ideal zero-mean Gaussian distribution and exhibit a super-Gaussian distribution. (2) Allan variance and PSD analysis reveal that flicker noise dominates the phase residuals. (3) The noise amplitudes are similar for all satellites, but certain differences are observed among different GNSS systems and satellite types. (4) The noise level of phase residuals is influenced by the receiver types, antenna types, and precise products from different analysis centers. Leveraging the error characteristics, the two-step Gaussian overbounding (OB) method was employed to estimate the corresponding OB parameters of the phase residuals. The overbounding results demonstrate that, under similar conditions, phase residuals can be bounded by the calculated bound within the acceptable integrity risk after removing the detected outliers. Anomaly monitoring experiments further show that phase residuals can effectively capture anomalies in precise satellite corrections, with the set threshold successfully detecting such anomalies.

Abstract

Accurately modeling and prediction the nonlinear motion of GNSS (Global Navigation Satellite System) coordinate time series holds significant theoretical and practical value for the study of geodynamics. A novel integrated network, named Ensemble Learning method based on Signal Source Driver (ELSSD), is proposed, which leverages the strengths of Long Short-Term Memory (LSTM) and Deep Self-Attention Neural Network (DSANN), while integrating GNSS loading data as an additional data source. Additionally, a multi-track synchronous sliding window data processing strategy is designed to address the challenge of multi-source data fusion input. The effectiveness of this algorithm is validated using GNSS coordinate time series from 186 global stations over a period of 10 years. Experimental results initially illustrate that, when accounting for displacement caused by environmental loading effects, there is a marked improvement in the modeling and prediction accuracy compared with GNSS input-only. Furthermore, the application of three ensemble network strategies-Bagging, Boosting, and Stacking-have further been demonstrated to enhance modeling and prediction accuracy. Compared with LSTM and DSANN networks, the proposed ELSSD algorithm achieves an average RMSE (Root Mean Square Error) of 3.6 mm for both modeling and prediction, with modeling accuracy improvements of 4.8% and 6.2%, while prediction accuracy improvements of 5.4% and 5.9%, respectively. With respect to the traditional Least Square method, there is an improvement of 22.1% and 27.9% in modeling and prediction accuracy, respectively. Regarding noise characteristics, there is a significant reduction in colored noise amplitude, with decreases of 36.7% and 36.0% observed in modeling and prediction, respectively. Simultaneously, the velocity uncertainty experiences an average reduction of 27.1% and 27.5%. The average velocity differences are measured at 0.06 mm/year and 0.24 mm/year, respectively. Hence, our findings suggest that the ELSSD algorithm emerges as an effective methodology for handling multi-source data input in GNSS coordinate time series, presenting promising practical applications in the field.

Abstract

The predictive learning of total electron content (TEC) spatiotemporal sequences aims to generate future TEC maps by learning from historical data, where both the spatial appearances and temporal variations are crucial for accurate predictions. However, the state-of-the-art TEC map prediction models typically employ sequential stacking of ConvLSTM, ConvGRU, and their variants. These models focus more on modeling temporal variations, and the spatial features extracted from the historical sequence are highly abstracted, resulting in the fine-grained spatial appearances not being adequately memorized or transmitted, leading to fuzzy prediction results during storm time. In this paper, we used PredRNN to propose a storm-time ionospheric TEC spatiotemporal prediction model with multichannel features, named Multichannel PredRNN, which can simultaneously remember the temporal patterns and spatial appearances in input sequence. The temporal memory as well as the spatial memory are updated repeatedly over time, ensuring that both temporal memory and spatiotemporal memory are fully utilized in prediction. According to Dst index, 60 magnetic storm events from 2011 to 2019 were selected as the dataset. We first discussed the impact of feature combinations on predictive performance. The results show that using multichannel feature (TEC + Dst&F10.7), the Multichannel PredRNN and the comparison models ConvGRU and ConvLSTM have the best prediction performance. Then we used the optimal feature combination for prediction. We compared Multichannel PredRNN with IRI-2016, COPG, ConvLSTM and ConvGRU under various conditions, including the entire test magnetic events, periods of quiet and storm, different phases of geomagnetic storms, and the most severe geomagnetic storms. Finally, we compared the performance of different output steps. The experimental results indicate that in all cases, Multichannel PredRNN with dual memory state and zigzag flow is superior to four compared models.

Abstract

Data-driven technologies have shown promising potential for improving GNSS positioning, which can analyze observation data to learn the complex hidden characteristics of system models, without rigorous prior assumptions. However, in complex urban areas, the input observation data contain task-irrelevant noisy GNSS measurements arising from stochastic noise, such as signal reflections from tall buildings. Moreover, the problem of data distribution shift between the training and testing phases exists for dynamically changing environments. These problems limit the robustness and generalizability of the data-driven GNSS positioning methods in urban areas. In this paper, a novel deep reinforcement learning (DRL) method is proposed to improve the robustness and generalizability of the data-driven GNSS positioning. Specifically, to address the data distribution shift in dynamically changing environments, the robust Bellman operator (RBO) is employed into the DRL optimization to model the deviations in the data distribution and to enhance generalizability. To improve robustness against task-irrelevant noisy GNSS measurements, the long-term reward sequence prediction (LRSP) is adopted to learn robust representations by extracting task-relevant information from GNSS observations. Therefore, we develop a DRL method with robust augmented reward sequence prediction to correct the rough position solved by model-based methods. Moreover, a novel real-world GNSS positioning dataset is built, containing different scenes in urban areas. Our experiments were conducted on the public dataset Google smartphone decimeter challenge 2022 (GSDC2022) and the built dataset Guangzhou GNSS version 2 (GZGNSS-V2), which demonstrated that the proposed method can outperform model-based and state-of-the-art data-driven methods in terms of generalizability across different environments.

Abstract

The prerequisite for achieving highly reliable Wide-Lane (WL) ambiguity resolution (AR) is the accurate determination of phase fractional cycle biases (FCB) on both the receiver and satellite side. It is generally assumed that the observation signal biases on the receiver side are stable, and receiver FCBs can be expressed using a single parameter over a period. However, due to the influence of satellite signal distortion and multipath errors, the receiver-dependent pseudorange biases (RDPB) may be inconsistent for different observed satellites at a certain receiver, which is called inconsistent RDPB (IRDPB) in this study. To improve the WL AR performance in GNSS network processing, we propose an optimized FCB estimation method with IRDPB corrected by the receiver individual. Utilizing data from 490 stations with four receiver types, the effect of the proposed WL FCB is verified in terms of ambiguity residual distribution and ambiguity fixed rate by comparing it with the original FCB and FCB with IRDPB corrected by receiver type. The proposed method improves the proportions of WL residuals within ± 0.1 cycles by 13.7%–20.5% for GPS, BDS-2 and BDS-3 compared to the original FCB. Compared to FCB corrected with receiver-type IRDPB, the number of stations with the proportions of residuals within ± 0.1 cycles in the percentage range (80,100] are improved by more than 130 for GPS, BDS-2 and BDS-3. Using the proposed FCB, the GNSS stations can obtain reliable real-time WL fixing solution. The result also shows that the influence of IRDPB varied with receiver types and GNSS systems. Galileo was less affected by IRDPB than GPS and BDS-2/3, and Trimble Alloy receivers suffer more significant IRDPB than the other three types of receivers.

Abstract

GPS flex power can improve anti-jamming capability by enhancing the transmitting power of individual signals. However, during the active periods of GPS flex power in 2020, it was found that the accuracy of kinematic orbit for GRACE-FO satellites is decreased. In this paper, the impact of flex power on kinematic orbit determination of GRACE-FO is investigated. With the analysis of 30-day epoch-differenced geometry-free combinations of phase, i.e., \(\:\varDelta\:{{\Phi\:}}_{\text{G}\text{F}}\) and signal-to-noise ratio (SNR) for GRACE-FO satellites, a new strategy which considers the impact of flex power on the continuity of ambiguity is put forward to improve the kinematic orbit of GRACE-FO. After considering flex power, the 3D root-mean-square (RMS) of GRACE-C and GRACE-D are reduced to 4.10 and 4.42 cm, with improvements of 36% and 21%, respectively. The improvements of SLR validation are 34% and 14% for GRACE-C and GRACE-D. The above results prove the effectiveness of the proposed strategy.

Abstract

Signal anti-jamming has always been a difficult problem in GNSS (global navigation satellite system) signal processing. There are many GNSS anti-jamming techniques in the existing research, which can achieve good results if the interferences are sparsely distinguishable in some signal feature domains. Specifically, the single antenna based anti-jamming techniques cannot deal with wideband Gaussian noise interference because it is not sparse in time or frequency domain, while the only effective method currently is using multiple antennas to apply the space array processing (SAP) technique since the wideband Gaussian noise interference is sparse in the spatial domain. However, when the incoming directions of the different interferences are not less than that of antennas, the interferences are not sparse to the array anymore, and the SAP anti-jamming performance would decrease drastically. In this paper, a LSTM (long short-term memory) deep neural network (DNN) based algorithm is proposed to enhance the array anti-jamming performance in this situation. The proposed network estimates the interferences as an integrity by exploring the non-linear relationship of the array data received by antennas. Especially, a new loss function is designed exclusively for GNSS anti-jamming problem. The proposed DNN method is verified in the simulation that two wideband Gaussian interferences with JSR (jamming to signal ratio) 50 dB can be eliminated by using two antennas’ data, and the interference cancellation ratio improvement is about 24 dB compared to some other widely used classical SAP algorithms.

Abstract

Traditional ionospheric modeling is inseparable from dense Global Navigation Satellite System (GNSS) reference stations. In this study, based on definite linear variation characteristics of the ionosphere along the longitudinal and latitudinal directions, a regional ionospheric total electron content (TEC) fusion model was proposed using relatively sparse GNSS linear stations beyond 100 km. Compared with the inverse distance weighting model using two adjacent stations with 100 km distance and three surrounding stations with 30 km distance, the accuracy of the proposed model has an improvement by 39.6% and 55.6% respectively, reaching a root-mean-square error of 0.32 TECU (TEC Unit) at mid-latitudes in high solar activity year. In the low solar activity year, the accuracy of the proposed model also achieves a high accuracy of 0.24 TECU at mid-latitudes and 0.86 TECU at low-latitudes. Finally, the proposed model was verified by precise point positioning (PPP). Compared with the traditional PPP, the ionosphere model enhanced PPP can significantly shorten the convergence time from 22.1 to 10.3 min in the magnetic storm period, and from 23.2 to 8.8 min in the quiet period.

Abstract

Global navigation satellite system (GNSS) Doppler measurements are immune to cycle slips, providing a robust way to detect ionospheric irregularities. This study presents a novel approach to detect equatorial plasma bubble (EPB) using a support vector machine (SVM) algorithm based on the GNSS Doppler measurements. The input of the detector is the Doppler index (DI), which is extracted from the dual-frequency differential Doppler observations. Data from HKWS station located in Hong Kong during 2022 are employed to train the SVM model and validate its performance. The results show the trained SVM model achieves 96.7% validation accuracy of EPB detection. To assess the general capability of the model, EPB events throughout the entire year of 2023 are investigated at both the HKWS station and the HYDE station. The results show the performance of EPB detection by the SVM model using DI is comparable to that of by visually inspecting the total electron content time series based on GNSS carrier-phase measurements. In addition, the characteristics of EPB occurrence are also consistent to previous studies, suggesting the detection results are reliable.

Abstract

Integrated Water Vapor (IWV) is crucial in environmental research, offering insights into atmospheric dynamics. Direct IWV measurement is challenging, necessitating alternative estimation technologies. Existing methods including Global Navigation Satellite System (GNSS), radiosondes, water vapor radiometers (WVR), satellite remote sensing, and numerical weather models (NWM), have specific limitations. GNSS and WVR provide high precision and temporal resolution (e.g., 5 min) but are limited to specific locations. Radiosondes, while accurate, have sparse spatial distribution and low temporal resolution (e.g., twice daily). Satellite remote sensing offers broad spatial resolution but lower temporal resolution (hours to days) and reduced accuracy under cloudy conditions and due to satellite tracks. NWMs provide global hourly products but their accuracy depends on meteorological data and model precision.

This study introduces a regional IWV predictive model using Machine Learning to address these challenges. Utilizing IWV data from GNSS stations, the study develops a predictive model based on least squares support vector machine, which autonomously determines optimal parameters to enhance performance. The model enables accurate IWV estimation at any location within a region, using inputs such as latitude, longitude, altitude, and temperature, achieving an average root mean square error of 0.95 mm. The model’s performance varies across seasons and terrains, showing adaptability to diverse conditions. The model’s reliability is validated by comparing its predictions with the conventional ERA5 IWV method, showing a 61% improvement rate. This refined IWV estimation model is applied for regional climate analysis, demonstrating its practical utility in environmental research, specifically for the Upper Rhine Graben Region.

Abstract

The real-time ionospheric data streams are continuously being provided by a number of International GNSS service analysis centers such as Centre National d’Etudes Spatiales (CNES), Chinese Academy of Sciences (CAS), Universitat Politècnica de Catalunya (UPC), and Wuhan University, however, the performance evaluation of these Real-Time Global Ionosphere Map (RT-GIM) products is essential. We assess the quality and consistency of these RT-GIM products from the declining phase of solar cycle 24 (year 2017) to the maximum of solar cycle 25 (year 2024) by comparing with Final GIMs provided by Center for Orbit Determination in Europe (CODE) and Jason-3 altimetry satellite. The results suggest that during the low solar activity periods (2017–2022), all the RT-GIMs perform almost similar. However, the performance of the CNES and CAS RT-GIMs strongly deteriorates as the solar cycle proceeds towards the maximum (2022–2024) with annual RMS values remains between 9 and 7.5 TECU. The external validation vs Jason-3 during this maximum period suggested that the accuracy of the UPC RTGIMs is nearly identical to the final CODE GIMs at typically 4–10 TECU in standard deviation over oceans, while performance degradations are recorded for rest of the RTGIMs exhibiting high standard deviations. Results suggest that the high RMS errors in GIMs from CNES and CAS might be related to the geomagnetic inclination misalignments followed by the map projections as both maps form single peak along geomagnetic equator during high solar activities. In addition, under the presence of a severe G4-class geomagnetic storm, CNES RT-GIMs undergoes severe accuracy degradation across all continents recording a − 20 to − 40 TECU bias offset. Meanwhile, UPC RT-GIM remain the most consistent and stable performer (both, globally and over oceans) that provides accurate global ionospheric information which is promising for their applications in real-time precise GNSS positioning.

Abstract

Spoofing detection is an essential process in global navigation satellite system anti-spoofing. Signal quality monitoring (SQM) methods have been widely studied as simple and effective means to detect spoofing. However, the disadvantages of the existing SQM methods, such as long alarm times and low detection rates, necessitate the study of new methods. Therefore, to address these challenges, this paper proposes a novel SQM method based on a long short-term memory-detect (LSTM-Detect) model with a strong capacity for sequential signal processing. In particular, this method evaluates the distortion of the autocorrelation function (ACF) by the trained LSTM-Detect model for spoofing detection. The simulation results demonstrate that the LSTM-Detect model can detect a wide range of spoofing signals, varying in signal power advantages, code phase differences, and carrier phase differences. In the Texas Spoofing Test Battery datasets 2–6, the detection rate exceeds 98.5%, with an alarm time of less than 5 ms. Compared with five existing SQM methods, the LSTM-Detect model exhibits a more comprehensive spoofing detection performance.

Abstract

Low-cost (LC) Global Navigation Satellite System (GNSS) receivers are argued as an alternative solution to geodetic GNSS counterparts for different applications. Single-frequency low-cost (SF-LC) GNSS receivers have been in the market for many years while their inability to acquire GNSS observations in second frequency limited their use. A few years ago, dual-frequency low-cost (DF-LC) GNSS receivers with enhanced capabilities entered the mass market, considering the advancements they have been tested and evaluated by many researchers. Lastly, multi-frequency low-cost (MF-LC) GNSS receivers become available. With the ability to track more satellite signals, these GNSS receivers are expected to obtain better overall performance. This review article aims to analyze recent advances and applications of LC GNSS receivers. To provide answers to the research question relevant articles were selected and analyzed. From the reviewed articles, it was concluded that the performance of SF-LC and DF-LC GNSS receivers is comparable to that of geodetic counterparts only in open-sky conditions. However, in adverse conditions, the differences become more highlighted. In such environments, SF and DF-LC GNSS receivers face challenges not only with positioning quality but also with their proper work. Limited studies on MF-LC receivers have reported comparable observations and positioning performance to geodetic GNSS receivers. Despite drawbacks, LC GNSS receivers have been successfully applied in surveying, mapping, geodetic monitoring, precision agriculture, navigation, atmosphere monitoring, Earth surface monitoring, and other fields.

Abstract

Global ionospheric total electron content (TEC) map prediction is important for improving the accuracy of global navigation satellite systems. There are two main issues with the current TEC prediction: (1) The deep learning models used for TEC prediction are mainly designed using a stacked structure. When stacking multiple layers, the input data will undergo continuous multi-layer convolution operations, leading to the loss of fine-grained features and the degradation of model performance; (2) The model optimization methods for TEC prediction are relatively outdated, mainly using manual optimization or grid search methods. To address these two issues, an automatic framework for global TEC map prediction and optimization is proposed, named as CGAOA-STRA-BiConvLSTM. It includes a global TEC map prediction model, STRA-BiConvLSTM, which can simultaneously extract both coarse-grained and fine-grained spatiotemporal features. It also contains an optimization algorithm, CGAOA, to optimize the model. We first experimentally verified the effectiveness of CGAOA. Then, the effectiveness of STRA-BiConvLSTM was verified through ablation experiments. Finally, we conducted comparative experiments from multiple perspectives between our framework and 5 mainstream methods: C1PG, C2PG, ConvLSTM, ConvGRU, and ED-ConvLSTM. The results show that in all cases, the proposed CGAOA-STRA-BiConvLSTM outperforms the comparative models.

Abstract

On February 6, 2023 the Kahramanmaraş Earthquake Sequence caused significant ground shaking and catastrophic losses across south-central Türkiye and northwest Syria. These seismic events produced ionospheric perturbations detectable in Global Navigation Satellite System (GNSS) total electron content (TEC) measurements. This work aims to develop and incorporate a near-real-time (NRT) ionospheric disturbance detection method into JPL’s GUARDIAN system. Our method uses a Long Short-Term Memory (LSTM) neural network to detect anomalous ionospheric behavior, such as co-seismic ionospheric disturbances among others. Our method detected an anomalous signature after the second \(M_w\)  7.5 earthquake at 10:24:48 UTC (13:24 local time) but did not alert after the first \(M_w\)  7.8 earthquake at 01:17:34 UTC (04:17 local time), which had a visible disturbance of smaller amplitude likely due to lower ionization levels at night and potentially the multi-source mechanism of the slip.

Plain Language Summary Seismic activity, including the destructive Kahramanmaraş Earthquake Sequence on February 6, 2023 in the Republic of Türkiye, result in vertical ground displacement that cause atmospheric waves. These waves propagate upwards to the outer atmosphere, disturbing the ionospheric electron content. This disturbance impacts the signals broadcast by positioning satellites (such as GPS) and received by ground-based receivers. If the receiver position is known, the impact to these signals can be used to measure the electron density disturbance caused by these seismically-induced atmospheric waves. Such studies usually rely on being aware of the event a priori. Using deep learning neural networks, we instead aim to detect anomalous signals automatically. We propose to utilise this method to detect seismically-induced disturbances over a large geographical area. The detection method proposed in this paper successfully detected an anomalous event in the ionosphere approximately ten minutes after the second earthquake in the Kahramanmaraş Earthquake Sequence.

Abstract

This study addresses the frequent convergence issues of satellite clocks within regional network, with a particular focus on the multifrequency advantages using data from 25 uniformly distributed reference stations across China. Experimental results demonstrate that incorporating the third frequency significantly enhances the accuracy of BDS-3 clock solutions, reducing the root mean square (RMS) by 44.5%. Additionally, employing a 2-min smoothing interval, multifrequency inclusion increases the wide-lane (WL) fixing rate by 30.4% at low elevation angles, which in turn leads to a marked improvement in narrow-lane (NL) ambiguity resolution. By leveraging phase-wide-lane observations, the stable wide-lane phase bias enables the continuous generation of inter-frequency clock bias (IFCB), ensuring reliable cyclic sequence construction even when satellites exit the observed region. The effectiveness of regional observable specific bias (OSB) on ambiguity resolution at the user level is highlighted, and over 95% of GPS, BDS-3, and Galileo NL fractional biases below 0.15 cycles could be achieved. Furthermore, the single-epoch convergence rates of multi-constellation precise point positioning (PPP) reach horizontal 91.9% and vertical 84.5% for multifrequency, a substantial improvement over the dual-frequency, which does not exceed 25%.

Abstract

In this paper, we propose a novel algorithm for fast frequency and phase synchronization of high-stability oscillators synchronized with 1 PPS signal from satellite navigation systems. The algorithm uses a model of a control object in the space of state variables and controls the frequency of an oscillator operating in a phase-locked loop. A new element is the introduction to the theoretical analysis and the design process, the time of entering synchronization. Currently, the literature lacks theoretical analysis and design methodology that considers the impact of the synchronization time on the choice of the steering algorithm and its parameters. All the data needed to determine the numerical values of the model were found experimentally for three different classes of control objects. Short synchronization times, a detailed description of the design methodology, and the use of values measured in the real system distinguish the proposed algorithm from the solutions described in the literature. The effect of optimization was achieved thanks to the algorithm’s two-stage operation. In the first stage, the algorithm aims to minimize the phase error quickly. The best solution for this stage is Sliding Mode Control (SMC). In the second stage, the algorithm strives to maximize the control quality, understood as minimizing the values of Maximum Time Interval Error (MTIE) and Time Deviation (TDEV). The Model Predictive Control (MPC) and Linear-Quadratic Regulator (LQR) optimal control algorithms were used at this stage. The paper also investigated the influence of the tuning parameters of these algorithms (weights as a function of cost) on the long-term behavior of the control system.

Abstract

In order to further improve the convergence rate and positioning accuracy of multi-frequency multi-system precise point positioning (PPP), an open-sourced software for fractional cycle bias (FCB) estimation (M_FCB) was produced based on MATLAB 2016a for GPS, BDS-2, Galileo, and BDS-3 satellite users. Based on raw frequency float ambiguity, the software can estimate ultra-wide-lane, wide-lane, narrow-lane combined FCB and raw frequency FCB. To validate the usability of the M_FCB software, 180 and 24 globally uniformly distributed multi-GNSS experiment stations were used to perform FCB estimation and triple-frequency uncombined PPP ambiguity resolution performance evaluation. The results show that the M_FCB software can generate stable and reliable FCB products. Particularly, Galileo satellites presented the best FCB stability. In addition, taking GPS/Galileo/BDS-2/BDS-3 fusion positioning as an example, the kinematic PPP after ambiguity resolution was significantly improved in terms of three-dimensional coordinate accuracy and positioning stability. Relative to the float solution, the average root mean square of the fixed-solution coordinate residuals in the east, north and vertical directions decreased by 30.3%, 12.5% and 16.0%, respectively.

Abstract

Terrestrial scale is one of the key datum parameters in the realization of the International Terrestrial Reference Frame (ITRF), which is defined by Very Long Baseline Interferometry (VLBI) and Satellite Laser Ranging (SLR) in the latest ITRF2020. Currently, the scale of GNSS is aligned to ITRF by estimating phase center offsets (PCOs) of GNSS satellites in a global adjustment with minimum constraints to ITRF. With the proposal of space tie concept in recent years, the co-location of different techniques on the same Low Earth Orbit (LEO) spacecraft provides a possible alternative to achieve this scale datum transfer between different techniques. In this study, we investigate the potential of terrestrial scale transfer between GNSS and SLR using the co-location onboard LEO satellites. The integrated precise orbit determination of GNSS and LEO satellites is performed based on one year of onboard GPS data and SLR observations from GRACE-FO and Swarm satellites as well as a global GNSS network. Two GNSS-only solutions and four GNSS + SLR combined solutions are generated. The results indicate that the scale determined by LEO gravitational constraint in the GNSS-only solution presents an average offset of -0.35 ppb w.r.t. ITRF2020. The space-ties onboard LEO satellites fail to transfer SLR scale information to GNSS network. With the inclusion of SLR observations to LEO satellites, the scale factor of the combined solution is only changed by less than 0.05 ppb with respect to the GNSS-only solution. The small changes of a few millimeters in GPS PCO of the orbital radial direction for the combined solution also demonstrate that the GPS z-PCOs cannot inherit any SLR scale information through LEO co-locations. Meanwhile, we find that the range biases of GRACE-FO and Swarm satellites achieve a good consistency for the majority of SLR stations, since these satellites carry the same type of laser retroreflector arrays and can achieve comparable orbit accuracy. The result indicates that estimating a common range bias parameter is sufficient for GRACE-FO and Swarm when using the SLR observations from these satellites.