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Improvement of Real-Time Kinematic Positioning Using Kalman Filter-Based Singular Spectrum Analysis During Geomagnetic Storm for Thailand Sector
(2023-01-01)
Srisamoodkham, Worachai
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Ansari, Kutubuddin
;
Jamjareegulgarn, Punyawi
The ionospheric error is the largest error source of positioning, especially when electron density becomes high during geomagnetic storms. Real-time kinematic (RTK) positioning during the storm time often has higher fluctuation and noise errors in positioning. Therefore, in this work, a technique based on Kalman filter with implementation of singular spectrum analysis (namely KF-SSA) is anticipated for RTK positioning. The RTK drone data are collected around 50 min of interval (7.37 AM to 8.28 AM) on May 12, 2021, with respect to a base station located at 13.84° N, 100.29° E. The RTK positioning tests have been done to determine the positioning accuracy of the proposed algorithm. The simulated results reveal that SSA implication with KF showed very high-precision estimates and improves the positioning accuracy.
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Applicability of Klobuchar Model for STEC Estimation Over Thailand Region
(2023-01-01)
Srisamoodkham, Worachai
;
Ansari, Kutubuddin
;
Jamjareegulgarn, Punyawi
In the current study, we investigated the total electron content (TEC) variations over Thailand region by using five multi-constellation Global Navigation satellite system (GNSS) signals. The STEC variations for all multiple GNSSs were computed with well-known ionospheric Klobuchar model and compared with the STEC values of Global Ionospheric map (GIM) on a storm day (May 12, 2021), before as well as after this storm day. The results showed that most of the GIM STEC values (GSVs) were overestimated with the Klobuchar model STEC values (KMSVs) for all constellations, except QZSS. As for the QZSS, the KMSVs were closest to the GSVs. Likewise, the MS-SHF-based Klobuchar model (MSSHFKM method) was able to compensate the ionospheric delay effectively as compared to the original Klobuchar model up to 77.08% RMSE during the geomagnetic storm on May 12, 2021.
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Detecting Equatorial Plasma Bubbles on All-Sky Imager Images Using Convolutional Neural Network
(2022-01-01)
Srisamoodkham, Worachai
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Shiokawa, Kazuo
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Otsuka, Yuichi
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Ansari, Kutubuddin
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Jamjareegulgarn, Punyawi
This paper proposes initially to apply convolutional neural network (CNN) for detecting the equatorial plasma bubbles on the ASI images. The considered CNN model is the YOLO v3 tiny model under a deep learning API (Keras), running on top of the machine learning platform (TensorFlow). Our program for EPB detection is written in Python that is extended easily to combine into a space weather web site for detecting and notifying EPBs in our next step. The results show that the YOLO v3-based CNN can detect the EPBs in ASI images with different intensities obtained from many countries. The threshold is tested and selected to be 0.40 suitably for detecting the anomaly (EPB existence). The maximum anomalous value is selected to decide the EPB occurrence.
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Positioning Comparison Using GIM, Klobuchar, and IRI-2016 Models During the Geomagnetic Storm in 2021
(2022-01-01)
Srisamoodkham, Worachai
;
Ansari, Kutubuddin
;
Jamjareegulgarn, Punyawi
This paper compares the positioning accuracy obtained from the GIM VTEC, the Klobuchar model, and the IRI-2016 model at Chiang Mai and DPT9 stations, Thailand, during an intense geomagnetic storm of 2021 (on May 12, 2021). The results show that the diurnal variation of the Klobuchar modeled VTECs show the same trend as that of the observed GIM VTECs with the same peaks and the maximum deviation of 22.5% at 05:00 UT. Meanwhile, the IRI2016-predicted VTECs show its peak at 07:00 UT and are not available obviously during 13:00–21:00 UT due to the impact of this intense geomagnetic storm. Most of the ionospheric delays obtained from the Klobuchar model underestimate those of the GIM VTEC, whereas they overestimate those of GIM VTEC during after midnight and pre-sunrise period. At both stations, the mean ionospheric range delays of the GIM VTEC are highest during daytime period while those of the IRI-2016 model are largest during nighttime period. The positioning errors at higher latitude (CHMA station) are larger than those at lower latitude (DPT9 station).
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A New Method for Computing Ionogram-Based TEC Based on Digisonde data for Disaster Prevention
(2020-03-01)
Jamjareegulgarn, Punyawi
;
Duangsuwan, Sarun
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Supnithi, Pornchai
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Srisamoodkham, Worachai
This paper presents a new approach for calculating the ionogram-based total electron content so as to be applied alternatively for alarming and preventing the disasters, for example, earthquake, tsunami or other space objectives. The proposed ITEC is estimated using the analytical expression of NeQuick model, the autoscaled Digisonde data, and a new variable "m" of 1. The results are show that 1) the proposed B0 is close to the B0-obs of Digisonde compared to the B0-IRI and the B2bot of the NeQuick model, 2) the diurnal variation of B0-Pro is the same as that of B0-obs compared to those of B0-IRI and B2bot, 3) the proposed ITEC is also close to the ITEC of Digisonde and TEC-iri, excluding the observed GPS TEC, and 4) all of the studied TEC values behave similar diurnal variations. Since the proposed ITEC is based on the analytical functions, the improvement of TEC-B0-Pro can be conducted reliably in order to close to the GPS TEC possibly and apply it optionally to correct the positioning errors for GNSS and aviation systems.

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