• Journal of Advanced Navigation Technology
• /
• v.20 no.1
• /
• pp.23-28
• /
• 2016

In this paper, we verified accuracy of the satellite based augmentation system (SBAS) tropospheric delay correction model for the Korean region. We employed the precise data of the tropospheric zenith path delay (ZPD) which is provided by the international GNSS service (IGS). In addition, we compared the verification results with that of the Saastamoinen model and the Hopfield model. Consequently, the bias residual error of the SBAS tropospheric delay correction model is about 50 mm, whereas the Saastamoinen model and the Hopfield model are more accurate. This residual error by the tropospheric delay model can affect the SBAS user position accuracy, but there is no problem in SBAS accuracy requirement. If we modified the meteorological parameters for SBAS tropospheric model to appropriate in Korean weather environment, we can provide better SBAS service to the Korean user.

• Journal of Positioning, Navigation, and Timing
• /
• v.10 no.4
• /
• pp.271-278
• /
• 2021

In this paper, a tropospheric delay error was calculated by using meteorological data collect from weather station and Saastamoinen model, and an empirical variogram of the tropospheric delay in the Korean peninsula was estimated. In order to estimate the empirical variogram of the tropospheric delay according to weather condition, sunny day, rainy day, and typhoon day were selected as analysis days. Analysis results show that a maximum correlation range of the empirical variogram on sunny day was about 560 km because there is overall trend of the tropospheric delay. On the other hand, the maximum correlation range of the empirical variogram on rainy was about 150 km because the regional variation was large. Although there is regional variation when the typhoon exists, there is a trend of the tropospheric delay due to a movement of the typhoon. Therefore, the maximum correlation range of the empirical variogram on typhoon day was about 280 km which is between sunny and rainy day.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.19 no.2
• /
• pp.163-170
• /
• 2016

Tropospheric delay is one of the largest error source in pseudolite navigation system. Because a pseudolite is installed on the ground and transmits its signal to a user in the air or on the ground, the conventional tropospheric delay model developed for a satellite navigation doesn't work properly. In this paper, performance analysis of several pseudolite tropospheric delay models has been done using meteorological data. Based on the result, a new compensation method for Hopfield model has been proposed.

• Journal of Positioning, Navigation, and Timing
• /
• v.10 no.4
• /
• pp.371-377
• /
• 2021

This research proposes the best combination of tropospheric delay models for Korean Positioning System (KPS). The overall results are based on real observation data of Japanese Quasi-Zenith satellite system (QZSS), whose constellation is similar to the proposed constellation of KPS. The tropospheric delay models are constructed as the combinations of three types of zenith path delay (ZPD) models and four types of mapping functions (MFs). Two sets of International GNSS Service (IGS) stations with the same receiver are considered. Comparison of observation residuals reveals that the ZPD models are more influential to the measurement model rather than MFs, and that the best tropospheric delay model is the combination of GPT3 with 5 degrees grid and Vienna Mapping Function 1 (VMF1). While the bias of observation residual depends on the receivers, it still remains to be further analyzed.

• Journal of Positioning, Navigation, and Timing
• /
• v.4 no.1
• /
• pp.33-41
• /
• 2015

Compact Network Real-Time Kinematic (RTK) is a method that combines compact RTK and network RTK, and it can effectively reduce the time and spatial de-correlation errors. A network RTK user receives multiple correction information generated from reference stations that constitute a network, calculates correction information that is appropriate for one's own position through a proper combination method, and uses the information for the estimation of the position. This combination method is classified depending on the method for modeling the GPS error elements included in correction information, and the user position accuracy is affected by the accuracy of this modeling. Among the GPS error elements included in correction information, tropospheric delay is generally eliminated using a tropospheric model, and a combination method is then applied. In the case of a tropospheric model, the estimation accuracy varies depending on the meteorological condition, and thus eliminating the tropospheric delay of correction information using a tropospheric model is limited to a certain extent. In this study, correction information modeling accuracy performances were compared focusing on the Low-Order Surface Model (LSM), which models the GPS error elements included in correction information using a low-order surface, and a modified LSM method that considers tropospheric delay characteristics depending on altitude. Both of the two methods model GPS error elements in relation to altitude, but the second method reflects the characteristics of actual tropospheric delay depending on altitude. In this study, the final residual errors of user measurements were compared and analyzed using the correction information generated by the various methods mentioned above. For the performance comparison and analysis, various GPS actual measurement data were collected. The results indicated that the modified LSM method that considers actual tropospheric characteristics showed improved performance in terms of user measurement residual error and position domain residual error.

• Journal of Positioning, Navigation, and Timing
• /
• v.2 no.1
• /
• pp.49-57
• /
• 2013

When pseudolite navigation system is applied to wide area, the tropospheric delay is the main error factor. In this study, we experimentally compared and analyzed the performance of the conventional pseudolite tropospheric delay models. The integration method using radiosonde meteorological data was suggested to derive the reference value for the comparison and analysis. Flight tests were carried out to analyze the performance of the tropospheric delay models according to the elevation angle and distance conditions between the user receiver and the pseudolite. As the results of this study, we provided the basis for the choice of tropospheric delay model appropriate to the relative location characteristics of the pseudolite and the user.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.6
• /
• pp.1317-1322
• /
• 2014

This paper shows effects of tropospheric delay models and mapping functions among delay features occurred when GPS code signal is transferred for GPS Time Transfer. GPS time transfer uses CGGTTS as the international standard format. For geodetic GPS receiver, ROB has provided r2cggtts software which generates CGGTTS data from RINEX data and all laboratories participated in TAI link use this software and send the CGGTTS results periodically. Though Saastamoinen zenith path model and Niell mapping function are commonly used in space geodesy, r2cggtts software applied NATO zenith path model and CHAO mapping function to the tropospheric delay model. Hence, this paper shows effects of two tropospheric delay models by implementing Saastamoinen model and Niell mapping function for the time offset.

• Journal of Positioning, Navigation, and Timing
• /
• v.7 no.3
• /
• pp.183-188
• /
• 2018

The Global Navigation Satellite System (GNSS) signal gets delayed as it goes through the troposphere before reaching the GNSS antenna. Various tropospheric models are being used to correct the tropospheric delay. In this study, we compared effectiveness of two popular troposphere correction models: Global Pressure and Temperature (GPT) and Satellite-Based Augmentation System (SBAS). One-year data from a particular site was chosen as the test case. Tropospheric delays were computed using the GPT and SBAS models and compared with the International GNSS Service tropospheric product. The bias of SBAS model computations was 3.4 cm, which is four times lower than that of the GPT model. The cause of higher biases observed in the GPT model is the fact that one cannot get wet delays from the model. If SBAS-based wet delays are added to the hydrostatic delays computed using the GPT model, then the accuracy is similar to that of the full SBAS model. From this study, one can conclude that it is better to use the SBAS model than to use the GPT model in the standard code-pseudorange data processing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.05a
• /
• pp.139-141
• /
• 2014

This paper shows effects of tropospheric delay models among delay features occurred when GPS code signal is transferred for GPS Time Transfer. GPS time transfer uses CGGTTS as the international standard format. For geodetic GPS receiver, ROB has provided r2cggtts software which generates CGGTTS data from RINEX data and all laboratories participated in TAI link uses the software and send the CGGTTS results periodically. Though Saastamoinen model and Niell mapping function are commonly used in space geodesy, r2cggtts software applied NATO model and CHAO mapping function to the tropospheric delay model. Hence, this paper shows effects of two tropospheric delay models implementing Saastamoinen model and Niell mapping function for the time offset.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.39 no.1
• /
• pp.23-28
• /
• 2021

Data analysis research using deep learning has recently been studied in various field. In this paper, we conduct a GNSS (Global Navigation Satellite System)-based meteorological study applying deep learning by estimating the ZWD (Zenith tropospheric Wet Delay) through MLP (Multi-Layer Perceptron) and LSTM (Long Short-Term Memory) models. Deep learning models were trained with meteorological data and ZWD which is estimated using zenith tropospheric total delay and dry delay. We apply meteorological data not used for learning to the learned model to estimate ZWD with centimeter-level RMSE (Root Mean Square Error) in both models. It is necessary to analyze the GNSS data from coastal areas together and increase time resolution in order to estimate ZWD in various situations.