GPS Satellite Differential Code Bias Research Articles

GPS satellite differential code bias estimation with current eleven low earth orbit satellites

GPS satellite differential code bias estimation with current eleven low earth orbit satellites

Improving GPS and Galileo precise data processing based on calibration of signal distortion biases

The quality of pseudorange observations plays an important role in Global Navigation Satellite System (GNSS) data processing, especially for satellite clock estimation, differential code bias (DCB) estimation and wide-lane ambiguity resolution. It is validated that receivers with different correlator spaces and front-end designs will bring signal distortion bias (SDB) in pseudorange observations, which will affect GNSS data processing based on inhomogenous receivers, such as receivers from Multi-GNSS EXperiment (MGEX) network. We firstly evaluate the consistency of GPS and Galileo satellite clock and DCB products among different International GNSS Service (IGS) Analysis Centers (ACs). The results show that inconsistency exists in both satellite clocks and DCB products among different ACs. For example, the difference of GPS satellite DCB (C1C-C1W) between two ACs, which is free of ionospheric delay, can reach up to 0.31 ns. To improve GPS and Galileo precise data processing based on inhomogenous receivers, pseudorange biases caused by receiver signal distortion are analyzed and calibrated for 5 receiver brands and 19 receiver models. The maximum pseudorange biases of ionospheric-free combination caused by receiver SDBs can reach up to ±3 ns and ±1 ns for GPS and Galileo, respectively. Then, bias corrections of GPS and Galileo raw pseudorange observations are divided into 11 groups and estimated by each receiver group. Finally, several experiments are carried out for the validation of GPS and Galileo SDB correction models. For example, the GPS and Galileo satellite clock offset differences estimated by different receiver brands are decreased by 86.59% and 50.0% for specific receiver type, respectively. The improvement ratio of satellite DCB accuracy is related to satellite systems and signal types, and the maximum improvement can reach up to 95%. Moreover, the maximum improvement of GPS undifferenced wide-lane ambiguity resolution success rate can reach up to 22% for specific receiver types. All these results prove that the bias corrections proposed could greatly eliminate the effect of receiver SDBs on GPS and Galileo precise data processing. Hence, we suggest the corrections of SDBs should be taken into consideration for IGS data reprocessing.

Simultaneous estimation of GPS P1-P2 differential code biases using low earth orbit satellites data from two different orbit heights

Global navigation satellite system (GNSS) differential code bias (DCB) is a significant error source in ionosphere modeling that uses GNSS observation data. Given that the orbit altitudes of low earth orbit (LEO) satellites are above the F layer of ionosphere, ionized electrons come from the upper ionosphere or plasmasphere, so observations suffer from small signal delays. DCBs can be estimated using LEO data instead of that from ground stations. In most studies, LEO-based DCB estimation employs onboard data from either a single LEO or from two that are at the same orbit height. In our study, we applied four LEO satellites data from two different orbit heights to estimate GPS satellite DCBs, receiver DCBs and LEO-based vertical total electron content (VTEC) parameters simultaneously. Before DCB estimation, a data preprocessing strategy suitable for LEO onboard data was applied to obtain clear pseudorange data. We developed a processing method for the upper ionosphere using LEO onboard data from different orbit heights, by introducing LEO-based VTEC models in advance to remove its effects; two introduced VTEC models were derived from modeling results using paired data from the same orbital height, respectively. We draw some conclusions as follows. For the LEO satellites at different orbit heights, the more the number of LEO satellites involved, the more stable estimation results achieved. We also noted that the GPS satellite DCBs stability and accuracy with established LEO-based VTEC models, using multi-LEOs data from different orbit heights, were better than the achievable results using onboard data from the same orbital height or using single LEO satellite schemes. Compared with the estimation results of single LEO solutions, the monthly stabilities and accuracies for multi-LEOs solutions from different orbit heights are improved by 25–35% approximately. Among all the tested schemes, superior stability derived from simultaneous estimation using four LEO data at different orbit heights is 0.064 ns; the optimal accuracies for DCB estimation for different GPS satellites are the scheme GRACE-A and JASON-2 (two LEOs) of 0.146 ns, approximately, compared with Center of Orbit Determination of Europe products, respectively. The stability and accuracy of LEO-based DCB estimations were approximately similar to those achieved using ground solutions.

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.

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.

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.

Global Ionosphere Mapping and Differential Code Bias Estimation during Low and High Solar Activity Periods with GIMAS Software

Ionosphere research using the Global Navigation Satellite Systems (GNSS) techniques is a hot topic, with their unprecedented high temporal and spatial sampling rate. We introduced a new GNSS Ionosphere Monitoring and Analysis Software (GIMAS) in order to model the global ionosphere vertical total electron content (VTEC) maps and to estimate the GPS and GLObalnaya NAvigatsionnaya Sputnikovaya Sistema (GLONASS) satellite and receiver differential code biases (DCBs). The GIMAS-based Global Ionosphere Map (GIM) products during low (day of year from 202 to 231, in 2008) and high (day of year from 050 to 079, in 2014) solar activity periods were investigated and assessed. The results showed that the biases of the GIMAS-based VTEC maps relative to the International GNSS Service (IGS) Ionosphere Associate Analysis Centers (IAACs) VTEC maps ranged from −3.0 to 1.0 TECU (TEC unit) (1 TECU = 1 × 1016 electrons/m2). The standard deviations (STDs) ranged from 0.7 to 1.9 TECU in 2008, and from 2.0 to 8.0 TECU in 2014. The STDs at a low latitude were significantly larger than those at middle and high latitudes, as a result of the ionospheric latitudinal gradients. When compared with the Jason-2 VTEC measurements, the GIMAS-based VTEC maps showed a negative systematic bias of about −1.8 TECU in 2008, and a positive systematic bias of about +2.2 TECU in 2014. The STDs were about 2.0 TECU in 2008, and ranged from 2.2 to 8.5 TECU in 2014. Furthermore, the aforementioned characteristics were strongly related to the conditions of the ionosphere variation and the geographic latitude. The GPS and GLONASS satellite and receiver P1-P2 DCBs were compared with the IAACs DCBs. The root mean squares (RMSs) were 0.16–0.20 ns in 2008 and 0.13–0.25 ns in 2014 for the GPS satellites and 0.26–0.31 ns in 2014 for the GLONASS satellites. The RMSs of receiver DCBs were 0.21–0.42 ns in 2008 and 0.33–1.47 ns in 2014 for GPS and 0.67–0.96 ns in 2014 for GLONASS. The monthly stability of the GPS satellite DCBs was about 0.04 ns (0.07 ns) in 2008 (2014) and that for the GLONASS satellite DCBs was about 0.09 ns in 2014. The receiver DCBs were less stable than the satellite DCBs, with a mean value of about 0.16 ns (0.47 ns) in 2008 (2014) for GPS, and 0.48 ns in 2014 for GLONASS. It can be demonstrated that the GIMAS software had a high accuracy and reliability for the global ionosphere monitoring and analysis.

Improving performance of GPS satellite DCB estimation for regional GPS networks using long-term stability

Compensation for differential code bias (DCB) is necessary because it is the major source of errors in total electron content (TEC) measurements. The DCB estimation performance is degraded when only the regional GPS network is used. Because DCB estimation is highly correlated with ionospheric modeling, this degradation is particularly evident for measurements concentrated in an area of high TEC concentration. This study proposes a DCB estimation method that uses the long-term stability of the DCB to improve the estimation performance of the regional GPS network. We estimate satellite DCBs by assuming their constancy over seven months. This extended period increases the number of measurements used in DCB estimation and changes the local time distribution of collected measurements. As a result, the unbalanced distribution of specific ionospheric conditions disappears. Tests are performed using both global and regional networks, and the estimation performance is evaluated based on the position error and pseudorange residuals. First, the difference between the global and regional networks when using the conventional method is analyzed. Second, proposed methods are applied to regional networks. The proposed method can improve the DCB estimation performance, and the results are similar to those obtained using one-day global network data.

Computation of GPS P1–P2 Differential Code Biases with JASON-2

GPS Differential Code Biases (DCBs) computation is usually based on ground networks of permanent stations. The drawback of the classical methods is the need for the ionospheric delay so that any error in this quantity will map into the solution. Nowadays, many low-orbiting satellites are equipped with GPS receivers which are initially used for precise orbitography. Considering spacecrafts at an altitude above the ionosphere, the ionized contribution comes from the plasmasphere, which is less variable in time and space. Based on GPS data collected onboard JASON-2 spacecraft, we present a methodology which computes in the same adjustment the satellite and receiver DCBs in addition to the plasmaspheric vertical total electron content (VTEC) above the satellite, the average satellite bias being set to zero. Results show that GPS satellite DCB solutions are very close to those of the IGS analysis centers using ground measurements. However, the receiver DCB and VTEC are closely correlated, and their value remains sensitive to the choice of the plasmaspheric parametrization.

A two-step ionospheric modeling algorithm considering the impact of GLONASS pseudo-range inter-channel biases

The Global Navigation Satellite System presents a plausible and cost-effective way of computing the total electron content (TEC). But TEC estimated value could be seriously affected by the differential code biases (DCB) of frequency-dependent satellites and receivers. Unlike GPS and other satellite systems, GLONASS adopts a frequency-division multiplexing access mode to distinguish different satellites. This strategy leads to different wavelengths and inter-frequency biases (IFBs) for both pseudo-range and carrier phase observations, whose impacts are rarely considered in ionospheric modeling. We obtained observations from four groups of co-stations to analyze the characteristics of the GLONASS receiver P1P2 pseudo-range IFB with a double-difference method. The results showed that the GLONASS P1P2 pseudo-range IFB remained stable for a period of time and could catch up to several meters, which cannot be absorbed by the receiver DCB during ionospheric modeling. Given the characteristics of the GLONASS P1P2 pseudo-range IFB, we proposed a two-step ionosphere modeling method with the priori IFB information. The experimental analysis showed that the new algorithm can effectively eliminate the adverse effects on ionospheric model and hardware delay parameters estimation in different space environments. During high solar activity period, compared to the traditional GPS + GLONASS modeling algorithm, the absolute average deviation of TEC decreased from 2.17 to 2.07 TECu (TEC unit); simultaneously, the average RMS of GPS satellite DCB decreased from 0.225 to 0.219 ns, and the average deviation of GLONASS satellite DCB decreased from 0.253 to 0.113 ns with a great improvement in over 55%.

Influences of the day‐night differences of ionospheric variability on the estimation of GPS differential code bias

AbstractThe estimation of differential code bias (DCB) of GPS system is one of the necessary steps for total electron content (TEC) derivation from GPS measurements. Usually, the method for estimating the GPS DCBs follows the assumption of the gentle temporal and spatial variation of the ionosphere, but this assumption is just an approximation because of the ionosphere's inherent variability. It has been indicated that the estimated GPS satellite DCBs are sometimes influenced by the ionospheric conditions. In this paper, we demonstrate a possible influence of ionospheric variability that differs between day and night on the estimated DCBs from measurements of a single GPS station. It is found that the average standard deviations (STDs) of the satellite DCBs estimated with daytime data are higher than that with the nighttime data. To reduce this day‐night difference effect on GPS DCB determination, we use an improved estimation method based on the primary features of the ionospheric variability with local time. A local time dependent weighting function was introduced into the original least squares DCBs estimation algorithm. A test with data for BJFS station (39.60°N, 115.89°E) in 2001 indicates that the STD of the DCBs decreases from 2.533 TECU (total electron content unit, 1 TECU = 1016 el m−2) to 2.308 TECU, or by 8.9%, after the improved method was applied. For comparison, another test for the same station in 2009 indicates that the STD decreases from 1.344 TECU to 1.295 TECU. The amplitude of the 2009 improvement is very limited, only about 3.6%. The difference of the percentage improvements can probably be attributed to the different ionospheric conditions between 2001 and 2009.

Two-step method for the determination of the differential code biases of COMPASS satellites

The differential code bias (DCB) in satellites of the Global Navigation Satellite Systems (GNSS) should be precisely corrected when designing certain applications, such as ionospheric remote sensing, precise point positioning, and time transfer. In the case of COMPASS system, the data used for estimating DCB are currently only available from a very limited number of global monitoring stations. However, the current GPS/GLONASS satellite DCB estimation methods generally require a large amount of geographically well-dis- tributed data for modeling the global ionospheric vertical total electron content (TEC) and are not particularly suitable for current COMPASS use. Moreover, some satellites with unstable DCB (i.e., relatively large scatter) may affect other satellite DCB estimates through the zero-mean reference that is currently imposed on all satellites. In order to overcome the inadequacy of data sources and to reduce the impact of unsta- ble DCB, a new approach, designated IGGDCB, is developed for COMPASS satellite DCB determination. IGG stands for the Institute of Geodesy and Geophysics, which is located in Wuhan, China. In IGGDCB, the ionospheric vertical TEC of each individual station is independently modeled by a gener- alized triangular series function, and the satellite DCB refer- ence is selected using an iterative DCB elimination process. By comparing GPS satellite DCB estimates calculated by the IGGDCB approach based on only a handful (e.g., seven) of tracking stations against that calculated by the currently existing methods based on hundreds of tracking stations, we are able to demonstrate that the accuracies of the IGGDCB- based DCB estimates perform at the level of about 0.13 and 0.10 ns during periods of high (2001) and low (2009) solar activity, respectively. The iterative method for DCB reference selection is verified by statistical tests that take into account the day-to-day scatter and the duration that the satellites have spent in orbit. The results show that the impact of satellites with unstable DCB can be considerably reduced using the IGGDCB method. It is also confirmed that IGGDCB is not only specifically valid for COMPASS but also for all other GNSS.

A Study on the Reliability of the Ionospheric VTEC and Satellite DCB Derived from a Regional GPS Network

According to the requirement of high-precision satellite navigation, we have introduced the method for the quasi-realtime monitoring of variations of the regional ionospheric total electron content (TEC) and GPS satellite differential code bias (DCB), based on the dual-frequency carrier-phase smoothed pseudorange data obtained from a regional GPS network in China. Especially, we have studied the feasibility of retrieving DCB independently from the regional GPS networks with different sizes. For this purpose, 3 regional networks based on the countrywide GPS stations are investigated. The comparisons of the computed DCB and VTEC(vertical total electron content) with those of CODE(the Center for Orbit Determination in Europe) indicate that in order to realize a reliable quasi-realtime measurement of DCB by a regional network, there is a certain requirement on the size of the regional network, and that the relative accuracies of the quasi-realtime VTEC and DCB measured by using a Chinese GPS regional network can reach 2.0TECu and 0.25ns, respectively.