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Performance improvement of the GAGAN satellite-based augmentation system based on local ionospheric delay estimation in Thailand
(2022-10-01)
Sophan, Somkit
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Myint, Lin M.M.
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Saito, Susumu
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Supnithi, Pornchai
Satellite-Based Augmentation System (SBAS) is essential to support aircraft navigation. L1 SBAS operates on the L1 frequency (1575.42 MHz) and is currently still of interest since all GNSS satellites and receivers do not fully support additional frequencies such as L5 (1176.45 MHz). Although the Global Positioning System (GPS) aided Geo Augmented Navigation (GAGAN) SBAS is available, the performances are degraded due to the discrepancies of the ionospheric correction over Thailand and surrounding areas. Hence, in this work, we propose a new method based on the geometry-free ionospheric delay estimation with a single frequency (L1) and a single reference station requirement. The local ionospheric delays are estimated based on the proposed method with the observed GPS and GAGAN data in Thailand. Then the ionospheric corrections are obtained from the estimated local ionospheric delays. The analysis shows that using the estimated corrections, the positioning errors are reduced both on quiet days and locally disturbed days in 2019. More reductions in the positioning errors are found in September and December than other months. In addition, we perform a preliminary availability assessment of two critical phases of flights. The GAGAN performances with the proposed method for the APV-I and LPV-200 categories are improved up to 57% and 53%, respectively, in comparison with the baseline method of the IGP correction.
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Neural Network Prediction of Receiver Bias in Ionospheric Delay Computation
(2022-01-01)
Thu, Phyo C.
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Supnithi, Pornchai
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Min Myint, Lin Min
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Saito, Susumu
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Saekow, Apitep
An important measure typically used to understand ionosphere properties and disturbances is total electron content (TEC). A typical approach to calculating the ionospheric TEC is by analyzing dual-frequency GPS data. Satellite and receiver biases are the primary discrepancies in TEC computation. In this work, we develop a neural network to predict the instrumental receiver bias based on slant TEC. The minimum standard deviation method is used to calculate the receiver bias. Neural network with two hidden layers is trained with datasets and then used to predict the receiver bias. The predicted receiver bias from the proposed neural network differs from the baseline method by about 10 to 20 percent.
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Improvement of single point positioning accuracy by using GAGAN satellite-based augmentation system in Thailand Region
(2021-05-19)
Sophan, Somkit
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Phakphisut, Watid
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Myint, Lin M.M.
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Supnithi, Pornchai
Although GAGAN satellite-based augmentation system (SBAS) provides ionospheric correction service to India and surrounding areas, the correction values do not cover the entire region of Thailand and even at provided grids, they may not be sufficiently accurate. Hence, this work, we propose a local ionospheric delay estimation method based on the geo-free ionospheric delay estimation. Then the estimated ionospheric delays are applied together with the fast and long-term corrections of GAGAN SBAS to improve the positioning errors. The results show that the estimated ionospheric delays can improve the user positioning errors in terms of horizontal and vertical errors up to 0.5 and 1 meter, respectively.
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Performances of GAGAN Satellite-Based Augmentation System in Thailand Region
(2020-07-01)
Sophan, Somkit
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Phakphisut, Watid
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Myint, Lin M.M.
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Supnithi, Pornchai
The ASEAN IVO project currently supports the research related to GNSS and ionospheric data products for disaster prevention and aviation in low-latitude regions. Satellite-Based Augmentation System (SBAS) is vital to air navigation in many regions around the world. In Thailand, the L1-frequency SBAS corrections can be received from the GPS Aided Geo Augmented Navigation (GAGAN) system which is intended for use over Indian airspace. In this work, we analyze the performances of the GAGAN system in Thailand on quiet and disturbed days in March 2019 by applying the entire corrections received at King Mongkut's Institute of Technology Ladkrabang station. The 95-percent horizontal and vertical accuracies on quiet days are 1.52 and 3.18 meters, respectively. In contrast, on disturbed days they are 1.97 and 3.41 meters, respectively.
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Comparison of the gnss position accuracy on mobile phones in Vientiane, laos
(2019-07-01)
Soumphonphakdy, Dethnaphonexay
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Southisombath, Khanpui
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Supnithi, Pornchai
In this research, the position errors derived from Global Navigation Satellite System (GNSS) signals of two different brands of smart phones are assessed. To perform the experiment, two smartphones Samsung Galaxy Note 8 and Google Pixel 1 have been chosen. The main goal of this test is to compare the position accuracy of each phone. As different environments such as open-sky or partially blocked view affect the received satellite signal levels; therefore, we made the test by recording the GNSS signals in crowded city areas, where many buildings can be found, and open areas, where there are no obstructions. The raw data are processed using the recently available open-source software named Google Analysis Tools. The results show that the received GPS signal in the clear environment have lower errors and more accurate than in the city environment. It can be seen that Galaxy Note 8 has the ability to locate places better (less error values) than Google Pixel.
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New receiver bias calculation for total electron content (TEC) in Bangkok, Thailand
(2018-07-02)
Tongkasem, Napat
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Supnithi, Pornchai
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Phakphisut, Watid
Ionospheric delay is an important parameter which affects the positioning accuracy of Global Navigation Satellite System (GNSS). It can be analyzed from the total electron content (TEC) in the signal propagation path, computed from the code and carrier phase of at least dual-frequency GNSS signals. Although TEC models have previously been developed by several models including the Global Ionospheric Map (GIM), International Reference Ionosphere (IRI) and Klobuchar model for equatorial region, the estimation of TEC may not be as accurate as in other regions due to relatively fewer research studies as well as unique ionospheric characteristics at these latitudes. Generally, computed slant TECs need to be adjusted for satellite and receiver biases, the latter is dependent upon receivers and receiver location. Therefore, this study proposed the new receiver bias calculation algorithm which can deal with these problems including cycle slips, outlier data, and missing data. In particular, the TEC is computed from code pseudo range only, while the receiver bias is computed from both code and carrier phase together with the elevation angle restriction above 60 degrees (to reduce the cycle slip issue). To verify the proposed algorithm, TEC values at KMIT station (latitude = 13.73o, longitude = 100.77o), located in an equatorial region, in 2016 are estimated during 4 seasons. The TEC results are with those of International GNSS Service (latitude = 13.74o, longitude = 100.5o) and the global ionospheric map (at grid position: latitude = 13.7o, longitude = 100o) based on the Root Mean Square (RMS) error. The results evidently demonstrated that the averages of RMS are 20.60% and 10.99% for GIM and IGS, respectively.
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A correction factor of bottomside thickness parameter for computing TEC in global navigation satellite systems
(2017-10-19)
Jamjareegulgarn, Punyawi
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Supnithi, Pornchai
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Hozumi, Kornyanat
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Tsugawa, Takuya
This paper proposes two new equations for computing the bottomside thickness parameter of the NeQuick 2 model with a correction factor (B2bot Pro2) and the simulated TEC values (TEC Pro). The main contributions of this work are twofold, i.e., 1) the proposed B2bot Pro2 equation can be used to compute the bottomside thickness whose trends and values are close to ones of the observed B0 (B0 obs) obtains from DPS-4 (Digisonde) and 2) the computed B2bot Pro2 are used to compute the TEC values without additional TEC observation by any devices and TEC computation. In this case, it is useful for some locations where there exist only ground-based ionosonde without TEC observation or TEC measurement doesn't work in some situations. The results show that the B2bot Pro2 have the same trends as the B0 obs. They are closer to the B0 obs, except at 13LT in June solstice and September equinox. The averages of absolute differences between B2bot Pro2 and B0 obs (avAD Pro2) are generally lower than about 8 km. They show that the B2bot Pro2 are close to the B0 obs with the improved percentages of higher than 80%. The TEC computed using the B2bot Pro2 equation (TEC Pro) in the nighttime are generally close to the observed TEC (TEC obs) compared with those in the daytime.
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Estimation of the single GPS-receiver bias using the gradient descent algorithm
(2016-09-06)
Chiablaem, Athiwat
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Supnithi, Pornchai
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Klinngam, Somjai
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Panachart, Chaiwat
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Saekow, Apithep
The ionospheric Total Electron Content (TEC) can be obtained from processing measurements of the dual-frequency Global Positioning System (GPS) receiver. The main sources of errors in the TEC calculation are satellite and receiver biases. In this paper, we apply the gradient descent algorithm on the receiver bias estimation. The TEC is derived from measurements at 12 dual-frequency GPS stations in Thailand. The criterion of receiver bias estimation is based on the minimum sum of the vertical TEC (VTEC) standard deviation method. The results show that the maximum receiver bias value is approximately 3.69 ns at UDON station, while the minimum value is -5.91 ns at SRTN station. The accuracy of the receiver biases from this algorithm is compared with the reference method. The maximum percentage deviation is about 7.5% at SRTN station. The percentage deviation of the minimum sum of the VTEC between the reference method and the proposed method from all stations are less than 0.05%. Thus, the proposed algorithm is a viable option to estimate the receiver bias.
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Predawn plasma bubble cluster observed in Southeast Asia
(2016-06-01)
Watthanasangmechai, Kornyanat
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Yamamoto, Mamoru
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Saito, Akinori
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Tsunoda, Roland
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Yokoyama, Tatsuhiro
Predawn plasma bubble was detected as deep plasma depletion by GNU Radio Beacon Receiver (GRBR) network and in situ measurement onboard Defense Meteorological Satellite Program F15 (DMSPF15) satellite and was confirmed by sparse GPS network in Southeast Asia. In addition to the deep depletion, the GPS network revealed the coexisting submesoscale irregularities. A deep depletion is regarded as a primary bubble. Submesoscale irregularities are regarded as secondary bubbles. Primary bubble and secondary bubbles appeared together as a cluster with zonal wavelength of 50 km. An altitude of secondary bubbles happened to be lower than that of the primary bubble in the same cluster. The observed pattern of plasma bubble cluster is consistent with the simulation result of the recent high-resolution bubble (HIRB) model. This event is only a single event out of 76 satellite passes at nighttime during 3–25 March 2012 that significantly shows plasma depletion at plasma bubble wall. The inside structure of the primary bubble was clearly revealed from the in situ density data of DMSPF15 satellite and the ground-based GRBR total electron content.
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TEC prediction with neural network for equatorial latitude station in Thailand
(2012-01-01)
Watthanasangmechai, Kornyanat
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Supnithi, Pornchai
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Lerkvaranyu, Somkiat
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Tsugawa, Takuya
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Nagatsuma, Tsutomu
This paper describes the neural network (NN) application for the prediction of the total electron content (TEC) over Chumphon, an equatorial latitude station in Thailand. The studied period is based on the available data during the low-solar-activity period from 2005 to 2009. The single hidden layer feed-forward network with a back propagation algorithm is applied in this work. The input space of the NN includes the day number, hour number and sunspot number. An analysis was made by comparing the TEC from the neural network prediction (NN TEC), the TEC from an observation (GPS TEC) and the TEC from the IRI-2007 model (IRI-2007 TEC). To obtain the optimum NN for the TEC prediction, the root-mean-square error (RMSE) is taken into account. In order to measure the effectiveness of the NN, the normalized RMSE of the NN TEC computed from the difference between the NN TEC and the GPS TEC is investigated. The RMSE, and normalized RMSE, comparisons for both the NN model and the IRI-2007 model are described. Even with the constraint of a limited amount of available data, the results show that the proposed NN can predict the GPS TEC quite well over the equatorial latitude station. Copyright © The Society of Geomagnetism and Earth, Planetary and Space Sciences (SGEPSS).

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