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A new intersystem doubledifference RTK model applicable to both overlapping and nonoverlapping signal frequencies
Satellite Navigation volume 4, Article number: 22 (2023)
Abstract
Aiming at the problem that the traditional intersystem doubledifference model is not suitable for nonoverlapping signal frequencies, we propose a new intersystem doubledifference model with single difference ambiguity estimation, which can be applied for both overlapping and nonoverlapping signal frequencies. The single difference ambiguities of all satellites and Differential InterSystem Biases (DISB) are first estimated, and the intrasystem double difference ambiguities, which have integer characteristics, are then fixed. After the ambiguities are successfully fixed, highprecision coordinates and DISB can be obtained with a constructed transformation matrix. The model effectively avoids the DISB parameter filtering discontinuity caused by the reference satellite transformation and the low precision of the reference satellite single difference ambiguity calculated with the code. A zerobaseline using multiple types of receivers is selected to verify the stability of the estimated DISB. Three baselines with different lengths are selected to assess the positioning performance of the model. The ionosphericfixed and ionosphericfloat models are used for short and mediumlong baselines, respectively. The results show that the Differential InterSystem Code Biases (DISCB) and Differential InterSystem Phase Biases (DISPB) have good stability regardless of the receivers type and the signal frequency used and can be calibrated to enhance the strength of the positioning model. The positioning results with three baselines of different lengths show that the proposed intersystem doubledifference model can improve the positioning accuracy by 6–22% compared with the intrasystem doubledifference model which selects the reference satellite independently for each system. The Time to First Fix (TTFF) of the two mediumlong baselines is reduced by 30% and 29%, respectively.
Introduction
Global Navigation Satellite System (GNSS) has entered a multiconstellation and multifrequency era (Tao et al., 2022), which can provide more powerful positioning services, but also increases the complexity of data processing (Teunissen & Khodabandeh, 2022). Compared with a single system, multiple systems can position with higher accuracy and reliability (Xiao et al., 2020). Similarly, multifrequency data can also improve the positioning performance ( Wu et al., 2022). More and more attention has been paid to the highprecision positioning technology of multifrequency and multisystems.
Doubledifference RealTime Kinematic (RTK) is a commonly used model of highprecision positioning. In multisystem RTK positioning, each system usually selects its reference satellite, which is called the intrasystem double difference model (Robert Odolinski & Teunissen, 2020). Compared with the intrasystem double difference model, the intersystem double difference model uses a common reference satellite for all the systems, resulting in more observations, which can theoretically enhance the strength of the model and improve the positioning accuracy (Chen et al., 2021; Paziewski & Wielgosz, 2015). The intersystem doubledifference model must consider the effect of Differential InterSystem Biases (DISB). DISB is caused by different systems related to the receiver hardware delay (Paziewski & Wielgosz, 2015).
Odijk and Teunissen (Odijk & Teunissen, 2013) analyzed the stability of DISB, and the results show that DISB is stable for the overlapping signal frequency regardless of the receiver types. The influence of DISB can be ignored when the receiver type is the same. Gao et al. (Gao et al., 2018) analyzed the DISB characteristic for the nonoverlapping signal frequency, and the results show that the DISB for nonoverlapping frequency cannot be ignored, but has good stability independent of receiver types.
Intersystem RTK models are usually divided into two types: the DISBfloat and DISBfixed, according to the ways of handling DISB (Zhao et al., 2022). The DISBfloat model estimates the DISB parameters together with the receiver coordinates and ambiguity parameters. In the single epoch mode, the DISBfloat model has the same model strength as the intrasystem RTK model (Wu et al., 2018). In the multiepoch mode, the stability of the DISB parameter can be used to improve the model strength and positioning accuracy. Odijk and Teunissen (Odijk & Teunissen, 2013) used the method of parameter renormalization to integrate the double difference ambiguity between reference satellites into the DISB parameters, and the influence of the reference satellite transformation must be considered in the multiepoch mode. This method is suitable for overlapping signal frequencies, and the influence of the single difference ambiguity of the reference satellite must be considered for nonoverlapping frequencies (Jia et al., 2019). Mi et al. (2019) proposed an intersystem RTK model based on a single difference model, which can be adapted for nonoverlapping signal frequencies. Both single difference and double difference DISBfloat model use the feature of stable multiepoch DISB to improve the positioning accuracy.
The DISBfixed model uses a priori DISB to correct the model, and higher model strength and positioning accuracy can also be obtained in the singleepoch mode. Tian et al. (2017) used particle filter to estimate DISB based on the DISBfixed model, which does not require a priori DISB. Sui et al. (2018) used particle swarm optimization algorithm to search DISB based on the DISBfixed model, which does not require a priori DISB. The principle of these two methods is to obtain the DISB parameter with the largest ratio value as the true value (Rui Shang et al., 2021a, 2021b), which creates the DISB halfcycle problem (Tian et al., 2017). The halfcycle problem seriously affects the stability of the estimated DISB parameters and the positioning accuracy. Zhao et al. (2021) proposed a method to effectively avoid the InterSystem Biases (ISB) halfcycle problem by transforming the search space of ISB.
In this contribution, we propose a new intersystem double difference RTK model with single difference ambiguity estimation that can be applied for both overlapping and nonoverlapping signal frequencies. In this model, a satellite among multiple systems is selected as the common reference satellite to form the intersystem double difference observation equation. Compared with the traditional intrasystem double difference RTK model, the number of observation equations is increased, and theoretically higher positioning accuracy can be obtained. Compared with other intersystem double difference models suitable for nonoverlapping signal frequencies, the proposed model does not need to calculate the single difference ambiguity of the reference satellite in advance. Because the model estimates the single difference ambiguity, the influence of the reference satellite transformation is not considered. Because of the existence of DISB, the intersystem double difference ambiguity no longer has integer characteristics, so we form the intrasystem double difference ambiguity which has integer characteristics. After the ambiguity is successfully fixed, the highprecision coordinates and DISB can be obtained with a constructed transformation matrix. Benefiting from the stability of the DISB parameters, with the multiepoch observations the model can achieve better positioning results than the traditional intersystem model. We first evaluate the stability of DISB using this model, which is a prerequisite for precise positioning. We finally use this model with the ionosphericfixed and ionosphericfloat to test the positioning performance with the baselines of different lengths.
In the next section, a new intersystem doubledifference model with single difference ambiguity estimation is introduced. The model is divided into the ionosphericfixed model and ionosphericfloat model according to different ionospheric processing modes. Subsequently, we analyze the DISB stability of dual frequencies signals for Global Positioning System (GPS), BeiDou Navigation Satellite System (BDS), Galileo navigation satellite system (Galileo), which contains overlapping and nonoverlapping signal frequencies, and multiple types of receivers are also considered. Finally, we test the positioning performance with the proposed model using three baselines of different lengths, a short baseline and two mediumlong baselines.
Methods
This section first introduces the proposed intersystem doubledifference model. The model can be extended to multiple systems and multiple frequencies, but only if these frequencies are allowed to be combined (Tian et al., 2018). In addition, to make the model applicable to the baselines of different lengths, we also introduce the ionosphericfixed and ionosphericfloat models.
Ionosphericfixed intersystem doubledifferenced model
The double difference RTK model can eliminate the effects of the ionosphere and troposphere for short baselines (Odolinski et al., 2014). The ionosphericfixed singledifferenced observation equations are formulated as
where \(b\) and \(r\) represent two different Global Navigation Satellite System (GNSS) receivers, \(S\) represents GNSS satellite, \(A\) represents a satellite system, \(i\) represents frequency, \(\Delta P\) and \(\Delta \varphi\) represent the observed single difference code and single difference phase in meters, and \(\lambda\) represents wavelength. We further have the single differences between receivers \(\Delta \rho_{br}^{{s_{A} }}\) (distance), \(\Delta {\text{d}}{t_{br}}\) (clock error), \(\Delta d_{br,i}^{A}\) (code hardware delay), \(\Delta \Phi_{br,i}\) (initial phase bias), \(\Delta \delta_{br,i}^{A}\) (carrier phase hardware delay), \(\Delta N_{br,i}^{{s_{A} }}\) (integer ambiguity), and \(\Delta e\) and \(\Delta \varepsilon\) represent the code and phase measurement errors.
A satellite is selected as the reference satellite for both systems, and the intrasystem and intersystem doubledifferenced observation equations are formed simultaneously. The ionosphericfixed intersystem doubledifferenced observation equations are formulated as follows:
where \(\Delta \nabla\) is the doubledifference operator, \(1_{A}\) and \(s_{A}\) represent the reference and nonreference satellite of system \(A\), respectively. The hardware delay and other parameters are reorganized as follows:
where \(b_{{\text{DISCB}}}\) and \(b_{{\text{DISPB}}}\) represent Differential InterSystem Code Biases (DISCB) and Differential InterSystem Phase Biases (DISPB), respectively. The state vector is rearranged as follows:
Since the single difference ambiguity and the DISB parameter are linearly dependent, this equation is rank deficient. A feasible approach is to use the Sbasis method to reorganize the linearly dependent parameters (Khodabandeh & Teunissen, 2016; Odijk et al., 2016). In this paper we assign an approximate initial value and an approximate initial variance to all the state parameters (Takasu & Yasuda, 2009). It is important to point out that only at the initial epoch, the value and variance of the state vector need to be assigned. After obtaining the initial values and variances of all the states, we can use the Kalman filter to update measurements to obtain the float solutions of the state parameters. Although we obtain float solutions for the parameters, there is still a linear dependence between them. We need constructing a transformation matrix to reorganize the linearly dependent parameters. This is equivalent to the Sbasis method. The difference is that the Sbasis method reorganizes the parameters before parameter estimation, while the method in this paper reorganizes the parameters after parameter estimation. Due to the existence of DISB, the single difference ambiguity is not directly composed of the intersystem double difference ambiguities. We can construct a transformation matrix to convert the single difference ambiguity into the intrasystem double difference ambiguity. This is because the intrasystem ambiguity parameters are not affected by DISB and the intrasystem double difference ambiguity still has the integer characteristics. The transformation matrix D is constructed as follows:
where \({\varvec{O}}\) is zero matrix.
We assume that both systems A and B have n observable satellites in (5). It is used to convert the single difference ambiguity into the double difference ambiguity, and the converted double difference ambiguity is composed of the single difference ambiguities of two same systems, not of two different systems. This is the same as the traditional intrasystem double difference RTK model. Compared with the traditional intrasystem double difference model, this model adds the intersystem double difference observation equation. The addition of the intersystem double difference observation equation also introduces DISB parameter, not directly increasing the model strength. However, this model can use the time stability of ISBs and impose time constant constraints on ISBs by Kalman filter to improve the accuracy of the residual parameter solution. After using the Least Squares Ambiguity Decorrelation Adjustment (LAMBDA) (Teunissen, 1995) to fix the ambiguity, the high precision solution can be obtained by the following formula:
where \(\hat{\user2{N}}\) and \(\tilde{\user2{N}}\) represent the fixed and float solutions of the ambiguity parameter, respectively, \(\hat{\user2{U}}_{N}\) and \(\tilde{\user2{U}}_{N}\) represent the fixed and float solutions of the nonambiguity parameter, respectively, and \({\varvec{Q}}\) represents the covariance matrix.
However, since DISB is linearly dependent of the ambiguity parameter, using (6) can only obtain the ambiguity fixed solution for the coordinate parameters, not for DISB. This makes impossible to analyze the stability of DISB. To obtain the ambiguity fixed solution of DISPB parameters, we must reorganize the parameters to eliminate the linear correlation between DISB and ambiguity. The ambiguity and DISB parameters are reorganized as follows:
The state vector after parameter reorganization is represented as follows:
where \(\tilde{\user2{U}}_{N}\) represents all the nonambiguity parameters, including the coordinates and DISBs, and \(\tilde{\user2{X}}\) contains the \(\tilde{\user2{U}}_{N}\) and ambiguity parameters. Finally, we present the transformation matrix \({\varvec{F}}\) from (4) to (8):
where \(1_{{\text{DISPB}}}\) represents the coefficient 1 corresponding to the state parameter DISPB, and \(\left\{ {  \lambda_{i} /\lambda_{j} } \right\}_{RA}\) represents the coefficient \(\left\{ {  \lambda_{i} /\lambda_{j} } \right\}\) corresponding to single difference ambiguity of the reference satellite of system A.
Formula 5 can convert the single difference ambiguity without integer characteristics into the intrasystem double difference ambiguity with integer characteristics. After fixing the ambiguity, (6) can be used to obtain the fixed solution for high precision coordinates. However, (5) eliminates the linear correlation between ambiguity parameters, but not between DISB and ambiguity parameters, so accurate DISB cannot be obtained. Through the derivation of formula (7), we can construct the transformation matrix (10). The transformation matrix (10) contains (5), which means that it eliminates the linear correlation not only between ambiguity parameters, but also between DISB and ambiguity. Thus, an absolute DISB can be obtained.
In summary, the Kalman filter is first used to obtain the float solution of (4), and the transition matrix (10) is subsequently used to convert the state vector (4) to (8), and the converted intrasystem double difference ambiguity is fixed. After the ambiguity is fixed successfully, the accurate coordinates and DISB are obtained by (6).
Ionosphericfloat intersystem doubledifferenced model
In a mediumlong baseline, the ionospheric and tropospheric errors cannot be completely eliminated because the two stations are far apart (Li et al., 2015). The ionosphericfloat singledifferenced observation equations are formulated as follows:
where \(I_{br,i}^{{s_{A} }}\) represents singledifference ionospheric delay, \(T_{r}\) represents tropospheric Zenith Wet Delay (ZWD) for receiver r, \(\mu_{i}^{{s_{A} }}\) is the frequency dependent ionospheric delay coefficient, and \(M_{b}^{{s_{A} }}\) is the tropospheric mapping function.
Similarly, one satellite is selected from the multiple systems to form the double difference observation equations. The ionosphericfloat intersystem doubledifference observation equations are formulated as follows:
The state vector is as follows:
Since the frequencies of satellites A and B may not be the same, the singledifference ionospheric delay is still estimated here. In this way, the destruction of the double difference ionospheric coefficient for nonoverlapping frequencies can be avoided. Similar to the ionosphericfixed intersystem doubledifferenced model, after the float solution of the state parameters is obtained with the Kalman filter, the F1 matrix is used for the transformation.
Similarly, after completing the transformation using the \({\varvec{F}}_{1}\) matrix, the LAMBDA method can be used to obtain the fixed solution.
Experimental results
We first test the stability of DISB using the proposed model. The positioning performance of the proposed model is then tested on three baselines with different lengths. The dualfrequency observation data of GPS/BDS/Galileo are used in all the experiments, and the sampling interval is 30 s.
Experiments for the stability analysis of DISB
In this section, the stability of DISCB and DISPB is analyzed, including L1–B1 and L2–B2 of GPS/ BDS as well as L1–E1 and L2–E5a of GPS/Galileo. It is worth noting that among these frequencies, only L1E1 are overlapping frequencies, and the others are all nonoverlapping frequencies. We used two sets of baseline data with the same and different types of receivers. The satellite cutoff elevation angle was set at 15 degrees. To obtain more accurate results, the single epoch model was used to estimate DISB (Gao et al., 2017).
Two sets of zero baseline data were used to test the stability of DISB. Their specific information is shown in Table 1. CUT0CUT2 uses the same type of receiver, and CUT0CUT1 uses different types of receivers.
Figures 1, 2, 3, and 4 illustrate the DISB time series for these two baselines. Their mean and STandard Deviations (STD) are in Table 2. Since the integer part of the DISPB is affected by the reference satellite transformation, we only focus on the fractional part of the DISPB (Shang et al., 2021a, 2021b).
Figures 1 and 2 illustrate the DISB time series of CUT0CUT2. This is a baseline with the same type of receivers. As can be seen in Fig. 2, both DISCB and DISPB are close to 0 for overlapping frequencies. However, the DISBs for the nonoverlapping frequencies are not close to zero and cannot be ignored. The DISB for both nonoverlapping frequency and overlapping frequency has good stability with the same type of receiver used.
Figures 3 and 4 illustrate the DISB time series of CUT0CUT1. This is a baseline with the different types of receivers. As can be seen from the figures, even for the overlapping frequency the DISB is not 0. Although the STD of DISB with different types of receivers are slightly larger than those with the same type, they are still within an admissible range. All in all, DISB has good stability regardless of the receiver type and the frequency of the signals used. In the multiepoch positioning mode, the stability of DISB can be used to enhance the strength of the model and thus improve the positioning accuracy.
Experiments for positioning accuracy
In this section, the intersystem doubledifference RTK model is tested using three baselines of different lengths and compared with the intrasystem RTK model. The baselines information is in Table 3. All of them have a sampling interval of 30 s. GPS/BDS/Galileo dual frequency data was used in the three baselines. Ionosphericfixed model is used to deal with the CUTBCUTC baseline and ionosphericfloat model is used to deal with the CUTBPERT and CUTBNNOR baselines. To further test the validity of the model, the satellites cutoff elevation angles were artificially set at 40 degrees to simulate a rare observation environment. Figure 5 presents the number of visible satellites for the three baselines.
Figure 6 shows the positioning errors with the intersystem RTK model and the traditional intrasystem RTK model in E, N and U directions for baseline CUTBCUTC. The ambiguity fixed rates are 100% for both intersystem RTK model and the traditional intrasystem RTK model. As can be seen from the figures, the intersystem RTK model gives higher positioning accuracy than intrasystem RTK model. The Root Mean Square (RMS) of positioning errors obtained with these two methods are shown in Table 4. The RMSs of the positioning errors in E, N and U directions are 0.20 cm, 0.23 cm, and 0.93 cm for intrasystem RTK model, and 0.18 cm, 0.18 cm, and 0.83 cm for intersystem RTK model, respectively. Compared with the intrasystem RTK model, the positioning accuracy of the intersystem RTK model in E, N and U directions is improved by 10%, 22% and 11%, respectively. The ambiguity fixed rate is 100% for both intersystem RTK model and intrasystem RTK model.
Figure 7 shows the positioning errors with the intersystem RTK model and the traditional intrasystem RTK model in E, N and U directions for baseline CUTBPERT. It is a mediumlong baseline with a length of 22 km, which means that it is difficult to fix the ambiguity at the beginning. Even with our proposed intersystem RTK model, it also requires a certain convergence time. The RMSs of positioning errors are shown in Table 5, which also includes large errors that are converging. The RMSs of the positioning errors in E, N and U directions are 3.35 cm, 1.40 cm, and 13.6 cm for intrasystem RTK model, and 3.14 cm, 1.25 cm, and 12.61 cm for intersystem RTK model, respectively. Compared with the intrasystem RTK model, the positioning accuracy of the intersystem RTK model in E, N and U directions is improved by 6%, 11%, and 7%, respectively. In addition, we also counted the Time to First Fix (TTFF) of the two models. It is worth noting that only the time when the ambiguity is successfully fixed in 10 consecutive epochs is considered as the first fix. The intrasystem RTK model and the intersystem RTK model reach the first fix at the 66th and 46th epoch, respectively. Compared with the intrasystem RTK model, the TTFF of the intersystem RTK model is shortened by 30%. The ambiguity fixed rates of intrasystem RTK model and intersystem RTK model are 96% and 97%, respectively.
Figure 8 shows the positioning errors of the intersystem RTK model and the traditional intrasystem RTK model in E, N and U directions for baseline CUTBNNOR. It is a longer baseline, which means that it is more challenging to obtain accurate positioning results. Compared to the baseline CUTBPERT, the baseline CUTBNNOR requires a longer convergence time. The RMSs of positioning errors are shown in Table 6. The RMSs of the positioning errors in E, N, and U directions are 2.33 cm, 2.02 cm, and 12.28 cm for intrasystem RTK model, and 1.82 cm, 1.68 cm, and 9.84 cm for intersystem RTK model, respectively. Compared with the intrasystem RTK model, the positioning accuracy of the intersystem RTK model in E, N and U directions is improved by 22%, 17%, and 20%, respectively. The intrasystem RTK model and the intersystem RTK model reach the first fix at the 221st and 156th epoch, respectively, which is longer compared to baseline CUTBPERT. Compared with the intrasystem RTK model, the TTFF of the intersystem RTK model is shortened by 29%. The ambiguity fixed rates of intrasystem RTK model and intersystem RTK model are 91% and 94%, respectively.
Conclusions
In this study, we developed a new intersystem double difference RTK model with single difference ambiguity estimation that can be applied for both overlapping and nonoverlapping signal frequencies. By estimating the single difference ambiguity, the model effectively avoids the DISB parameter discontinuity caused by the reference satellite transformation. Compared with the traditional nonoverlapping frequency double difference model, it is not necessary to use the code observations to calculate the single difference ambiguity of the reference satellite in advance. We also present the ionosphericfloat mode and the ionosphericfixed mode of this model. This makes the model applicable not only to short baseline scenarios but also to mediumlong baseline scenarios. Specifically, we use this model to analyze the stability of DISB as well as the positioning performance.
The stability of DISCB and DISPB is tested extensively with multifrequency, multisystem, and multitype receiver. The conclusion is that both DISCB and DISPB have good stability, regardless of the frequency of the signal and the type of the receiver used. This means that the positioning accuracy with this model can benefit from the stability of DISCB and DISPB, which is the key reason for analyzing the stability of DISCB and DISPB.
We compare our proposed intersystem doubledifference RTK model with the traditional intrasystem doubledifference RTK model in terms of positioning accuracy using three baselines with different lengths. Benefiting from the stability of DISB, the proposed intersystem doubledifference model can improve the positioning accuracy by 6–22% compared with the intrasystem doubledifference model. The positioning results in two mediumlong baselines show that the TTFF of intersystem doubledifference RTK model is shortened by 30% and 29%, respectively, compared with that of traditional intrasystem doubledifference RTK model.
Availability of data and materials
The datasets that support the findings of this research are available from the corresponding author upon reasonable request.
Abbreviations
 DISB:

Differential intersystem biases
 DISCB:

Differential intersystem code biases
 DISPB:

Differential intersystem phase biases
 TTFF:

The time to first fix
 GNSS:

Global navigation satellite system
 BDS:

BeiDou navigation satellite system
 GPS:

Global positioning system
 RMS:

Rootmeansquare
 RTK:

Realtime kinematic
 ZWDs:

Zenith wet delays
 LAMBDA:

Least squares ambiguity decorrelation adjustment
 STD:

Standard deviations
References
Ge, C., Li, B., Zhang, Z., & Liu, T. (2021). Integer ambiguity resolution and precise positioning for tight integration of BDS3, GPS, GALILEO, and QZSS overlapping frequencies signals. GPS Solutions, 26, 26. https://doi.org/10.1007/s10291021012031
Gao, W., Gao, C., Pan, S., Meng, X., & Xia, Y. (2017). Intersystem differencing between GPS and BDS for mediumbaseline RTK positioning. Remote Sensing. https://doi.org/10.3390/rs9090948
Gao, W., Meng, X., Gao, C., Pan, S., & Wang, D. (2018). Combined GPS and BDS for singlefrequency continuous RTK positioning through realtime estimation of differential intersystem biases. GPS Solutions. https://doi.org/10.1007/s1029101706875
Jia, C., Zhao, L., Li, L., Gao, Y., & Gao, Y. (2019). Pivot singledifference ambiguity resolution for multiGNSS positioning with nonoverlapping frequencies. GPS Solutions, 23, 97. https://doi.org/10.1007/s1029101908916
Khodabandeh, A., & Teunissen, P. J. G. (2016). PPPRTK and intersystem biases: The ISB lookup table as a means to support multisystem PPPRTK. Journal of Geodesy, 90(9), 837–851.
Li, B., Feng, Y., Gao, W., & Li, Z. (2015). Realtime kinematic positioning over long baselines using triplefrequency BeiDou signals. IEEE Transactions on Aerospace and Electronic Systems, 51, 3254–3269. https://doi.org/10.1109/TAES.2015.140643
Mi, X., Zhang, B., & Yuan, Y. (2019). MultiGNSS intersystem biases: Estimability analysis and impact on RTK positioning. GPS Solutions. https://doi.org/10.1007/s1029101908738
Odijk, D., et al. (2016). On the estimability of parameters in undifferenced, uncombined GNSS network and PPPRTK user models by means of Ssystem theory. Journal of Geodesy, 90(1), 15–44.
Odijk, D., & Teunissen, P. J. G. (2013). Characterization of betweenreceiver GPSGalileo intersystem biases and their effect on mixed ambiguity resolution. GPS Solutions, 17, 521–533. https://doi.org/10.1007/s1029101202980
Odolinski, R., & Teunissen, P. J. G. (2020). Best integer equivariant estimation: Performance analysis using real data collected by lowcost, single and dualfrequency, multiGNSS receivers for short to longbaseline RTK positioning. Journal of Geodesy, 94, 91. https://doi.org/10.1007/s00190020014232
Odolinski, R., Teunissen, P. J. G., & Odijk, D. (2014). First combined COMPASS/BeiDou2 and GPS positioning results in Australia. Part II: Single and multiplefrequency singlebaseline RTK positioning. Journal of Spatial Science, 59, 25–46. https://doi.org/10.1080/14498596.2013.866913
Paziewski, J., & Wielgosz, P. (2015). Accounting for Galileo–GPS intersystem biases in precise satellite positioning. Journal of Geodesy, 89, 81–93. https://doi.org/10.1007/s0019001407633
Shang, R., Gao, C., Gao, W., Zhang, R., Peng, Z., & Liu, Q. (2021). MultiGNSS intersystem model for complex environments based on optimal state estimation. Measurement Science and Technology, 32, 054006. https://doi.org/10.1088/13616501/abdae5
Shang, R., Gao, C. F., Gao, W., Zhang, R. C., & Peng, Z. H. (2021). A single differencebased multiGNSS intersystem model with consideration of interfrequency bias and intersystem bias. Measurement Science and Technology. https://doi.org/10.1088/13616501/abbf0d
Sui, X. (2018). Research on the theory and method of intersystem double difference ambiguity forming and fixing for multiGNSS. Acta Geodetica et Cartographica Sinica, 47, 1160–1160.
Takasu, T., & Yasuda, A. (2009). Development of the lowcost RTKGPS receiver with an open source program package RTKLIB. In International Symposium on GPS/GNSS.
Tao, J., Chen, G., Guo, J., Zhang, Q., Liu, S., & Zhao, Q. (2022). Toward BDS/Galileo/GPS/QZSS triplefrequency PPP instantaneous integer ambiguity resolutions without atmosphere corrections. GPS Solutions, 26, 127. https://doi.org/10.1007/s10291022012873
Teunissen, P. J. G. (1995). The leastsquares ambiguity decorrelation adjustment: A method for fast GPS integer ambiguity estimation. Journal of Geodesy, 70, 65–82. https://doi.org/10.1007/BF00863419
Teunissen, P. J. G., & Khodabandeh, A. (2022). PPP–RTK theory for varying transmitter frequencies with satellite and terrestrial positioning applications. Journal of Geodesy, 96, 84. https://doi.org/10.1007/s00190022016652
Tian, Y., Ge, M., Neitzel, F., & Zhu, J. (2017). Particle filterbased estimation of intersystem phase bias for realtime integer ambiguity resolution. GPS Solutions, 21, 949–961. https://doi.org/10.1007/s1029101605843
Tian, Y., Liu, Z., Ge, M., & Neitzel, F. (2018). Determining intersystem bias of GNSS signals with narrowly spaced frequencies for GNSS positioning. Journal of Geodesy, 92, 873–887. https://doi.org/10.1007/s0019001711004
Wu, M., Zhang, X., Liu, W., Wu, R., Zhang, R., Le, Y., & Wu, Y. (2018). Influencing factors of GNSS differential intersystem bias and performance assessment of tightly combined GPS, Galileo, and QZSS relative positioning for short baseline. Journal of Navigation, 72, 1–22. https://doi.org/10.1017/S0373463318001017
Wu, Z., Wang, Q., Yu, Z., Hu, C., Liu, H., & Han, S. (2022). Modeling and performance assessment of precise point positioning with multifrequency GNSS signals. Measurement, 201, 111687. https://doi.org/10.1016/j.measurement.2022.111687
Xiao, G., Liu, G., Ou, J., Liu, G., Wang, S., & Guo, A. (2020). MGAPP: An opensource software for multiGNSS precise point positioning and application analysis. GPS Solutions, 24, 66. https://doi.org/10.1007/s10291020009761
Zhao, W., Liu, G., Gao, M., Lv, D., & Wang, R. (2022). INSassisted intersystem biases estimation and intersystem ambiguity resolution in a complex environment. GPS Solutions, 27, 3. https://doi.org/10.1007/s10291022013478
Zhao, W. H., Liu, G. Y., Wang, S. L., Gao, M., & Lv, D. (2021). Realtime estimation of GPSBDS intersystem biases: An improved particle swarm optimization algorithm. Remote Sensing. https://doi.org/10.3390/rs13163214
Acknowledgements
Many thanks are due to Curtin University and IGS for providing GNSS data. This work is funded by the National Key Research Program of China Collaborative Precision Positioning Project (No. 2016YFB0501900) and the National Natural Science Foundation of China (No. 41774017).
Funding
This work was jointly supported by the National Key Research Program of China Collaborative Precision Positioning Project (No. 2016YFB0501900) and the National Natural Science Foundation of China (Grant No. 41774017).
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Conceptualization, WZ; Data curation, MG; Formal analysis, GL and MG; Methodology, WZ; Software, MG; Supervision, GL; Validation, BZ and ML; Visualization, BZ and SH; Writing original draft, WZ; Writing review & editing, WZ and GL. All authors read and approved the final manuscript.
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Zhao, W., Liu, G., Gao, M. et al. A new intersystem doubledifference RTK model applicable to both overlapping and nonoverlapping signal frequencies. Satell Navig 4, 22 (2023). https://doi.org/10.1186/s43020023001127
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DOI: https://doi.org/10.1186/s43020023001127