• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.3-8
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• 2006

The next decade promises drastic improvements and additions to global navigation satellite systems (GNSS). Plans for GPS modernization include a civilian code measurement on the L2 frequency and a new L5 signal at 1176.45 MHz. Current speculations indicate that a fully operational constellation with these improvements could be available by 2013. Simultaneously, the Galileo Joint Undertaking is in the development and validation stages of introducing a parallel GNSS called Galileo. Galileo will also transmit freely available satellite navigation signals on three frequencies and is scheduled to be fully operational as early as 2008. In other words, a dual system receiver (e.g., GPS+GALILEO) for general users can access six civil frequencies transmitted by at least fifty eights navigation satellites in space. The advent of GALILEO and the modernization of GPS raise a lot of attention to the study of the compatibility and interoperability of the two systems. A number of performance analyses have been conducted in a global scale with respect to availability, reliability, accuracy and integrity in different simulated scenarios (such as open sky and urban canyons) for the two systems individually and when integrated. Therefore, the scope of this article aims at providing the technical benefits analysis for Taiwan specifically in terms of the performance indices mentioned above in a local scale, especially in typical urban canyon scenarios. The conclusions gained by this study will be applied by the Land Survey Bureau of Taiwanese as the guideline for developing future GNSS tracking facilities and dual GNSS processing module for precise surveying applications in static and kinematic modes.

• Journal of Satellite, Information and Communications
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• v.8 no.2
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• pp.29-35
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• 2013

In this paper, we investigate the type and detection methode of spoofing attack, and then analyze the performance of spoofing detection algorithm in GPS L1 signal through the simulation. Generally spoofer is different from the jammer, because the receiver can be operated and not. In case of spoofing the GPS receiver is hard to recognize the spoofing attack and can be operated normally without stopping because the spoofing signal is the mimic GPS signal. To evaluate the performance of spoofing detection algorithm, both the software based spoofing and GPS signal generator and the software based GPS receiver are implemented. In paper, we can check that spoofing signal can affect to the DLL and PLL tracking loop because code delay and doppler frequency of spoofing. The spoofing detection algorithm has been implemented using the pseudorange, signal strength and navigation solution of GPS receiver and proposed algorithm can effectively detect the spoofing signal.

• Korean Journal of Remote Sensing
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• v.35 no.4
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• pp.573-587
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• 2019

A multi-temporal approach using remotely sensed time series data obtained over multiple years is a very useful method for monitoring land covers and land-cover changes. While spectral-based methods at any particular time limits the application utility due to instability of the quality of data obtained at that time, the approach based on the temporal profile can produce more accurate results since data is analyzed from a long-term perspective rather than on one point in time. In this study, a multi-temporal approach applying a multi-periodic harmonic model is proposed for classification of remotely sensed data. A harmonic model characterizes the seasonal variation of a time series by four parameters: average level, frequency, phase, and amplitude. The availability of high-quality data is very important for multi-temporal analysis.An satellite image usually have many unobserved data and bad-quality data due to the influence of observation environment and sensing system, which impede the analysis and might possibly produce inaccurate results. Harmonic analysis is also very useful for real-time data reconstruction. Multi-periodic harmonic model is applied to the reconstructed data to classify land covers and monitor land-cover change by tracking the temporal profiles. The proposed method is tested with the MODIS and GOCI NDVI time series over the Korean Peninsula for 5 years from 2012 to 2016. The results show that the multi-periodic harmonic model has a great potential for classification of land-cover types and monitoring of land-cover changes through characterizing annual temporal dynamics.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.4
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• pp.315-333
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• 2021

Due to the Global Navigation Satellite System (GNSS) modernization, recently launched GNSS satellites transmit signals at various frequency bands such as L1, L2 and L5. Considering the Korean Positioning System (KPS) signal and other GNSS augmentation signals in the future, there is a high probability of applying more complex communication techniques to the new GNSS signals. For the reason, GNSS receivers based on flexible Software Defined Radio (SDR) concept needs to be developed to evaluate various experimental communication techniques by accessing each signal processing module in detail. This paper proposes a novel SDR-based A-GNSS receiver capable of processing multi-GNSS/RNSS signals at multi-frequency bands. Due to the modular structure, the proposed receiver has high flexibility and expandability. For real-time implementation, A-GNSS server software is designed to provide immediate delivery of satellite ephemeris data on demand. Due to the sampling bandwidth limitation of RF front-ends, multiple SDRs are considered to process the multi-GNSS/RNSS multi-frequency signals simultaneously. To avoid the overflow problem of sampled RF data, an efficient memory buffer management strategy was considered. To collect and process the multi-GNSS/RNSS multi-frequency signals in real-time, the proposed SDR A-GNSS receiver utilizes multiple threads implemented on a CPU and multiple NVIDIA CUDA GPGPUs for parallel processing. To evaluate the performance of the proposed SDR A-GNSS receiver, several experiments were performed with field collected data. By the experiments, it was shown that A-GNSS requirements can be satisfied sufficiently utilizing only milliseconds samples. The continuous signal tracking performance was also confirmed with the hundreds of milliseconds data for multi-GNSS/RNSS multi-frequency signals and with the ten-seconds data for multi-GNSS/RNSS single-frequency signals.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4D
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• pp.579-586
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• 2008

In this study, it was evaluated that the 3D tracking accuracy of basketball zone track line by real time GNSS satellite positioning methods which are VRS (Virtual Reference System), DGPS (Marine Differential GPS) and PP (Point Positioning) methods. The results of comparison between three methods over horizontal track of basketball zone, VRS, DGPS and PP methods showed ${\pm}$ several cm, ${\pm}$ m, and ${\pm}$ 2m horizontal position accuracy compared with real size respectively. And also, the grade and height deviation of the checking points on basketball zone by VRS method is very similar to the real grade and height, but results by DGPS and PP methods showed big variation and deviation in each case. We expected that VRS method using GNSS reference network will be a very useful tool compared with single based RTK method in real time accurate positioning such as precision construction fields, especially.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.761-768
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• 2023

In the 4th Industrial Revolution, AI(Artificial Intelligence) and IoT(Internet of Things) technologies are being applied to across various fields, with particularly prominence in asset management, disaster management, and meteorological observation. In these fields, it is necessary to accurately determine the real-time and precise tracking of the object's location and status, and to collect various data even in situations that are difficult to detect with existing sensors. In order to address these demands, the use of GNSS(Global Navigation Satellite System) is essential, and this technology enables the efficient management of assets, disaster prevent and response, and accurate weather forecasting. In this paper, we provide the investigated results for the latest trends in the application of GNSS in the fields of asset management, disaster management, and weather observation, among various fields incorporating AI and IoT and analyze them.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.1
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• pp.85-92
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• 2024

This study develops a Global Positioning System (GPS) Code Multipath Grid Map (CMGM) of each individual domestic reference station from the extracted code multipath of measurement data. Multipath corresponds to signal reflection/refraction caused by obstacles around the receiver antenna, and it is a major source of error that cannot be eliminated by differencing. From the receiver-independent exchange format (RINEX) data for two days, the associated code multipath of a satellite tracking arc is extracted. These code multipath data go through bias correction and interpolation to yield the CMGM with respect to the azimuth and elevation angles. The effect of the CMGM on multipath mitigation is then quantitatively analyzed to improve the Root Mean Square (RMS) of averaged pseudo multipath. Furthermore, the single point positioning (SPP) accuracy is analyzed in terms of the RMS of the horizontal and vertical errors. During two weeks in February 2023, the RMSs of the averaged pseudo multipath for five reference stations decreased by about 40% on average after CMGM application. Also, the SPP accuracies increased by about 7% for horizontal errors and about 10% for vertical errors on average after CMGM application. The overall quantitative analysis indicates that the proposed approach will reduce the convergence time of Differential Global Navigation Satellite System (DGNSS), Real-Time Kinematic (RTK), and Precise Point Positioning (PPP)-RTK correction information in real-time to use measurement data whose code multipath is corrected and mitigated by the CMGM.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.83-107
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• 2014

This study presents spatial characteristics of cloud using satellite image in the extreme heavy snowfall of the Yeongdong region. 3 extreme heavy snowfall events in the Yeongdong region during the recent 12 years (2001 ~ 2012) are selected for which the fresh snow cover exceed 50 cm/day. Spatial characteristics (minimum brightness temperature; Tmin, cloud size, center of cloud-cell) of cloud are analyzed by tracking main cloud-cell related with these events. These characteristics are compared with radar precipitation in the Yeongdong region to investigate relationship between cloud and precipitation. The results are summarized as follows, selected extreme heavy snowfall events are associated with the isolated, well-developed, and small-scale convective cloud which is developing over the Yeongdong region or moving from over East Korea Bay to the Yeongdong region. During the period of main precipitation, cloud-cell Tmin is low ($-40{\sim}-50^{\circ}C$) and cloud area is small (17,000 ~ 40,000 $km^2$). Precipitation area (${\geq}$ 0.5 mm/hr) from radar also shows small and isolated shape (4,000 ~ 8,000 $km^2$). The locations of the cloud and precipitation are similar, but in there centers are located closely to the coast of the Yeongdong region. In all events the extreme heavy snowfall occur in the period a developed cloud-cell was moving into the coastal waters of the Yeongdong. However, it was found that developing stage of cloud and precipitation are not well matched each other in one of 3 events. Water vapor image shows that cloud-cell is developed on the northern edge of the dry(dark) region. Therefore, at the result analyzed from cloud and precipitation, selected extreme heavy snowfall events are associated with small-scale secondary cyclone or vortex, not explosive polar low. Detection and tracking small-scale cloud-cell in the real-time forecasting of the Yeongdong extreme heavy snowfall is important.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.763-774
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• 2020

Wind data forecasted from the numerical weather prediction (NWP) model is generally used as the first-guess of the target tracking process to obtain the atmospheric motion vectors(AMVs) because it increases tracking accuracy and reduce computational time. However, there is a contradiction that the NWP model used as the first-guess is used again as the reference in the AMVs verification process. To overcome this problem, model-independent first guesses are required. In this study, we propose the AMVs derivation from Lucas and Kanade optical flow method and then using it as the first guess. To retrieve AMVs, Himawari-8/AHI geostationary satellite level-1B data were used at 00, 06, 12, and 18 UTC from August 19 to September 5, 2015. To evaluate the impact of applying the optical flow method on the AMV derivation, cross-validation has been conducted in three ways as follows. (1) Without the first-guess, (2) NWP (KMA/UM) forecasted wind as the first-guess, and (3) Optical flow method based wind as the first-guess. As the results of verification using ECMWF ERA-Interim reanalysis data, the highest precision (RMSVD: 5.296-5.804 ms^-1) was obtained using optical flow based winds as the first-guess. In addition, the computation speed for AMVs derivation was the slowest without the first-guess test, but the other two had similar performance. Thus, applying the optical flow method in the target tracking process of AMVs algorithm, this study showed that the optical flow method is very effective as a first guess for model-independent AMVs derivation.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1299-1310
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• 2018

In this study, we analyzed distribution and movement trends using in-situ observations and particle tracking methods to understand the movement of the drift ice in the Arctic Ocean. The in-situ movement data of the drift ice in the Arctic Ocean used ITP (Ice-Tethered Profiler) provided by NOAA (National Oceanic and Atmospheric Administration) from 2009 to 2018, which was analyzed with the location and speed for each year. Particle tracking simulates the movement of the drift ice using daily current and wind data provided by HYCOM (Hybrid Coordinate Ocean Model) and ECMWF (European Centre for Medium-Range Weather Forecasts, 2009-2017). In order to simulate the movement of the drift ice throughout the Arctic Ocean, ITP data, a field observation data, were used as input to calculate the relationship between the current and wind and follow up the Lagrangian particle tracking. Particle tracking simulations were conducted with two experiments taking into account the effects of current and the combined effects of current and wind, most of which were reproduced in the same way as in-situ observations, given the effects of currents and winds. The movement of the drift ice in the Arctic Ocean was reproduced using a wind-imposed equation, which analyzed the movement of the drift ice in a particular year. In 2010, the Arctic Ocean Index (AOI) was a negative year, with particles clearly moving along the Beaufort Gyre, resulting in relatively large movements in Beaufort Sea. On the other hand, in 2017 AOI was a positive year, with most particles not affected by Gyre, resulting in relatively low speed and distance. Around the pole, the speed of the drift ice is lower in 2017 than 2010. From seasonal characteristics in 2010 and 2017, the movement of the drift ice increase in winter 2010 (0.22 m/s) and decrease to spring 2010 (0.16 m/s). In the case of 2017, the movement is increased in summer (0.22 m/s) and decreased to spring time (0.13 m/s). As a result, the particle tracking method will be appropriate to understand long-term drift ice movement trends by linking them with satellite data in place of limited field observations.