Budtho, Jirapoom

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Nominal ionospheric delay gradient estimation at Suvarnabhumi airport, Thailand
(2017-10-19)
Budtho, Jirapoom
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Supnithi, Pornchai
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Saekow, Apitep
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Saito, Susumu
Ground-Based Augmentation System (GBAS) allows high-precision aircraft landing based on Global Navigation Satellite System (GNSS) at large airports. However, non-uniform spatial ionospheric delay needs to be determined. In this work, we compute the nominal ionospheric delay gradients around Suvarnabhumi airport, Thailand. The utilized techniques involve Kalman filter and LAMBDA method. Based on the measurements on DOY 043 of 2015, we found that the ionospheric delay gradients are less than 20 mm/km. With the improved ambiguity ratio test to obtain higher success rate than previous works, the standard deviation σ<inf>VIC</inf> is 5.27 mm/km.
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Multipath Analysis at Low-Latitude GNSS Stations around Suvarnabhumi Airport, Thailand, for GBAS Standards
(2021-01-01)
Budtho, Jirapoom
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Supnithi, Pornchai
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Saito, Susumu
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Siansawasdi, Nattapong
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Saekow, Apitep
The characteristics of the local area positioning error sources are important for Ground-Based Augmentation System (GBAS) service planning. Accurate standard deviation models are required for prior simulation of the performance of the aircraft precision landing system. The multipath standard deviation of the pseudo-range errors model is used in GBAS for each satellite elevation angle. This standard model is generated by collecting the multipath conditions from airports. However, some airports have different characteristics of the multipath effects than the others, resulting in inaccurate error models when applied to the GBAS operations. Therefore, in this work, we study and analyze a 1-dimensional curve-fitted model for the multipath error models at three GNSS stations near the Suvarnabhumi International Airport, Thailand. The results show that in the case that the multipath errors are distributed equally at each azimuth angle, the RMSEs are reduced from 0.1 to 0.02 meters near the 90-degree elevation angle and less than 0.05 meters at other degrees. For the AER1 station, located on the airport runway, in which the multipath errors are not distributed equally at each azimuth, the maximum RMSE, is less than 0.08 meters when compared with 0.14 meters from the GBAS model.
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Study on Effect of Equatorial Plasma Bubble over Real-Time Kinematic Positioning in Bangkok Thailand
(2022-01-01)
Thu, Phyo C.
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Supnithi, Pornchai
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Myint, Lin Min Min
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Budtho, Jirapoom
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Saito, Susumu
Equatorial plasma bubbles (EPBs) depict local ionospheric irregularity in low-latitude regions which can spread to mid-latitude regions. In this work, we analyzed the effects of the EPBs on the performance of real-time kinematic (RTK) positioning at the short, medium, and long baselines in Bangkok, Thailand. We used the kinematic positioning mode provided by a free and open-source software (FOSS) package called RTKLIB to analyze the positioning errors. It is found that the positioning errors are higher during the disturbance periods and more severe in the long baseline case.
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Instrumental Receiver Bias Estimation for Ionospheric Total Electron Content by Neural Network Model
(2023-10-01)
Thu, Phyo C.
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Supnithi, Pornchai
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Budtho, Jirapoom
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Saekow, Apitep
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Sopon, Thanomsak
Total Electron Content (TEC) is one of the most important parameters in the study of the ionosphere, especially for determining ionospheric disturbances. The TEC levels are typically estimated from dual-frequency GPS observation data. Since the measured TEC contains discrepancies such as satellite and receiver biases, they need to be removed to obtain more accurate TEC values. In this work, we estimate the receiver bias using a neural network technique. Based on the exhaustive evaluation, we design a neural network (NN) model with two-hidden layers, and it is trained with datasets from three GNSS observation stations in Thailand. The prediction from the proposed neural network deviates from the baseline reference using the minimum standard deviation method with significantly faster computational time. The trained NN model is also tested for estimating the receiver bias values at other untrained stations in Thailand.
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Ground Facility Error Analysis and GBAS Performance Evaluation Around Suvarnabhumi Airport, Thailand
(2024-02-01)
Budtho, Jirapoom
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Supnithi, Pornchai
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Siansawasdi, Nattapong
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Saito, Susumu
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Saekow, Apitep
The performances of the ground-based augmentation system (GBAS) designed for the landing phase of aircraft rely on the accurate characterization of error models. Among various error sources, the multipath model, which is typically constructed by combining environmental errors at airports, must be modeled in GBAS. However, in practice, the multipath effects at a particular airport differ from other airports due to distinct construction sites and continually changing environments, resulting in an inaccurate error model in GBAS operations. Therefore, in this article, we develop and evaluate a 2-D ground facility error model from the Global Navigation Satellite System Stations (GNSS) at the Suvarnabhumi International Airport in Bangkok, Thailand. The results indicate that the elevation and azimuth grid points require around seven days of observation data to create the GBAS ground facility error model for GBAS operation. The number of observations per day at each elevation and azimuth grid point will determine the data requirements for the complete building of the 2-D ground error model. When the proposed model is applied to the GBAS simulation, it is found that the proposed 2-D ground error model reduces the root-mean-square deviation (RMSD) of positioning errors by around 0.4% to 3.5% when compared to the 1-D error model and the category B Ground accuracy designator model, respectively. The maximum vertical protection level reduction of the proposed 2-D B-value model in comparison with the reference 1-D B-value is 0.24 m, about a 6% reduction.
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Budtho, Jirapoom

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