Budtho, Jirapoom

Preferred name

Budtho, Jirapoom

Alternative Name

Budtho, J.

Main Affiliation

School of Engineering

jirapoom.bu@kmitl.ac.th

Now showing 1 - 8 of 8

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Classification of Equatorial Ionospheric Irregularities Using Unsupervised Machine Learning Based on Spatiotemporal ROTI Keograms
(2025-01-01)
Mutasov, Gleb
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Supnithi, Pornchai
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Budtho, Jirapoom
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Tongkasem, Napat
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Nishioka, Michi
Equatorial ionospheric irregularities, particularly those associated with equatorial plasma bubbles (EPB), can significantly disrupt satellite navigation and communication systems. As the demand for reliable Global Navigation Satellite System (GNSS) and communication services grows, the prediction of ionospheric irregularities becomes critical. A key step in the prediction process is to identify distinct spatiotemporal patterns of irregularities, including day-to-day, longitudinal, and seasonal variations. However, with large datasets, manually classification or identification of these irregularities is a complex and challenging task. In this work, we propose unsupervised machine learning techniques to recognize and group irregularity patterns in large, unlabeled Rate of Total Electron Content (TEC) Index (ROTI) keograms. Specifically, two machine learning models: Gaussian Mixture Model and k-means clustering are employed. The ROTI keograms are constructed using GNSS data from two low-latitude receiver stations in Thailand. To reduce redundancy in the keogram images, three feature extraction techniques are applied before the clustering process. A comparative analysis is performed to determine the optimal number of clusters using these models. Based on the results, the optimal combination of feature extraction and clustering technique is determined for the proposed clustering model. The resulting k-means model with contour extractor classifies five distinct patterns of ionospheric irregularity patterns, providing valuable insights for enhancing EPB prediction models and deepening our understanding of ionospheric dynamics. Furthermore, these five irregularity patterns are analyzed in relation to space weather parameters such as the solar radio flux index (F10.7), and the geomagnetic index (Kp). The findings contribute to the development of robust prediction models, improving the reliability of satellite-based communication and navigation systems.
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Thermosphere–Ionosphere Responses Over Thailand During the 2015 St. Patrick's Day Storm: Comparison of Observed O/N2 and VTEC With the SD WACCM-X Model Outputs
(2025-10-01)
Jamlongkul, Paparin
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Wannawichian, Suwicha
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Paxton, Larry J.
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Cantrall, Clayton E.
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Liu, Han Li
We present the first comparative analysis of observational data and model results focusing on thermospheric-ionospheric responses over the Thailand region by studying the St. Patrick's Day geomagnetic storm on 17–18 March, 2015. This study aims to advance our understanding of regional responses by building on previous observation-model comparisons. The observational data include global O/N<inf>2</inf> ratios from GUVI onboard the TIMED spacecraft, global vertical total electron content (VTEC) from the worldwide GNSS receivers obtained from the Madrigal database, and regional VTEC over Thailand from the KMI6 GNSS station. The atmospheric simulations used are from SD WACCM-X, incorporating high-latitude drivers from the Weimer and Assimilative Mapping of Ionospheric Electrodynamics (AMIE) models. The O/N<inf>2</inf> comparison focuses on TIMED's overpasses across Thailand at 3 UT (10 LT) on both days. Both models tend to reproduce general trends in the O/N<inf>2</inf> ratio and VTEC variations prior to the storm onset. The SD WACCM-X/Weimer model shows better agreement with the O/N<inf>2</inf> ratio from GUVI observations over Thailand, particularly during the recovery phase. Meanwhile, the SD WACCM-X/AMIE model better captures VTEC trends on both large and localized scales, especially after sunset, and successfully reproduces localized features over Thailand. However, during the early recovery phase, both Weimer and AMIE drivers fail to fully capture the collapse of the equatorial ionospheric anomaly (EIA) as indicated by VTEC data, likely due to overestimated (Formula presented.) drift values at low latitudes.
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Analysis of Quiet Time Vertical Ionospheric Delay Gradients Around Suvarnabhumi Airport, Thailand
(2018-09-01)
Budtho, J.
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Supnithi, P.
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Saito, S.
Global Navigation Satellite System (GNSS) is vital to aircraft navigation at many phases of flight. To extend its use to the approach and landing phases, ground-based augmentation system is an important on-the-ground technology to reduce the positioning errors. However, nonuniform spatial ionospheric delays need to be assessed during ground-based augmentation system planning at each airport, particularly, in equatorial and low-latitude regions. In this work, we analyze the statistics of ionospheric delay gradients around Suvarnabhumi airport, Thailand. The ionospheric delay gradients are estimated using single-frequency code and carrier phase observation through the Kalman filter. To increase the success of the ratio test, the satellite elimination technique is then proposed. Based on the analysis between 2013 and 2016, we find that the background ionospheric delay gradients during equinox are higher than solstice, especially during September equinox 2013 when the gradients are about 9 mm/km. Moreover, the ionospheric delay gradients are more variable during daytime than nighttime.
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Deep learning-based prediction models for the vertical total electron content using GNSS satellite observations
(2026-08-01)
Mutasov, Gleb
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Myint, Lin Min Min
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Budtho, Jirapoom
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Perwitasari, Septi
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Nishioka, Michi
Ionospheric Total Electron Content (TEC) is a crucial parameter for characterizing the state of the ionosphere and assessing its impact on satellite-based navigation systems and on communication technologies. In equatorial and low-latitude regions, ionospheric irregularities, particularly equatorial plasma bubbles (EPBs), pose significant challenges for satellite navigation and communication due to their capacity to cause rapid TEC fluctuations and signal degradation. Since these effects are especially pronounced during ionospheric and geomagnetic disturbances, making accurate TEC prediction is an essential task for improving the reliability of GNSS-based positioning and space weather applications. This study presents a machine learning-based framework for one-day-ahead prediction of TEC with a 30-min resolution over the magnetic equator and low-latitude regions, with a focus on Southeast Asia. Unlike global models, our approach is tailored to local GNSS observations and directly predicts TEC values along specific satellite-receiver paths, defined by geographic location and satellite visibility. We integrate ionospheric pierce point (IPP) coordinates, geomagnetic indices, and solar activity indicators as features to enhance temporal and spatial forecasting accuracy. To address the nonlinear and nonstationary nature of TEC variations, we investigate and compare three deep learning architectures: a Transformer-based time-series model, a Temporal Kolmogorov–Arnold Network (TKAN), and a Long Short-Term Memory (LSTM). Additionally, the predictions are benchmarked against the empirical IRI-2020 model and a persistence baseline. The results demonstrate that both the Transformer and TKAN models outperform the LSTM and empirical approaches, particularly during different geomagnetic and ionospheric conditions, showing improved robustness and generalization. The proposed framework highlights the potential for accurate, resource-efficient TEC prediction in low-latitude regions and opens a pathway for further improvements by integrating multi-GNSS observations and additional space weather parameters.
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Statistical analysis and effects of radio frequency interference in GPS signal quality in Thailand
(2024-10-01)
Sophan, Somkit
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Supnithi, Pornchai
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Myint, Lin M.M.
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Budtho, Jirapoom
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Saito, Susumu
The radio frequency interference (RFI) in global navigation satellite system (GNSS) signals has recently received much attention in the GNSS community because of frequent jamming issues. The carrier-to-noise density ratio (C/N<inf>0</inf>) is one of the common parameters to indicate the signal quality. In this work, we propose a real-time RFI analysis based on windowing and normalization of C/N<inf>0</inf> observations. Specifically, the percentage of RFI values are analyzed based on the modified RFI detection. The steps to analyze the RFI levels (low, medium, high) are highlighted. In addition, we analyzed the occurrences of local RFI effects in areas surrounding the Suvarnabhumi International Airport as well as remote areas. We validate the modified RFI detection by using the GNSS reference stations at the urban, suburban, and outside the capital city in Thailand. The user positioning errors with the high (severe) RFI levels are investigated based on the single point positioning (SPP) and real-time kinematics (RTK). From the experimental simulations, the high RFI levels at the urban are higher than those at the suburban. As expected, the statistical analysis covering COVID-19 (2019 to 2023) shows that the high RFI levels in June 2023 (post COVID-19) are more than those in June 2020 and 2021 (lockdown COVID-19) by about twofold. Additionally, the SPP positioning errors with the medium/high RFI levels are clearly seen. There are more floating solutions in the RTK system in the year with more RFI presence.
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Single-Frequency Time-Step Ionospheric Delay Gradient Estimation at Low-Latitude Stations
(2020-01-01)
Budtho, Jirapoom
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Supnithi, Pornchai
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Saito, Susumu
The irregularity of the local-area ionospheric delay is a primary impediment for Ground-Based Augmentation System (GBAS) services. Excessive ionospheric delay gradients may degrade aircraft positioning for high precision landing systems. Therefore, the spatial gradients of the nominal background ionosphere must be studied as their statistics will be sent to the approaching aircraft. For the well-known station-pair method, ionospheric delay gradient estimation requires at least 2 Global Navigation Satellite System (GNSS) reference stations. This method can be applied to both single or dual-frequency GNSS receivers. However, when the GNSS stations are far apart, it is not suitable for estimating the ionospheric delay gradients at short baselines, and the time-step method is an attractive alternative. In this work, we propose a single-frequency time-step method for ionospheric delay gradient estimation. Careful baseline length selection is needed, due to ionospheric piercing point movements. We applied our method to GNSS data in 2014, at the peak of the 24th solar cycle, and showed that the standard deviations of the vertical ionospheric delay gradients were comparable to those derived from the dual-frequency time-step method. The standard deviations of vertical ionospheric gradients, ranged between 4 and 6 mm/km. The {VIG}}values around the equinoxes were 1.5 mm/km greater than at other times.
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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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