• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.57-59
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• 2006

In order to apply the network adjustment, the correct ambiguities between each of reference stations must be calculated. This paper focused on the comparison of ambiguity resolution between the reference stations. Three hours test data with different baseline length from 20 km to 60 km have been analyzed. The results show that the time-to-fix between 20 km and 40 km is typically under 1 minute. However, over 60km baseline, the time-to-fix performance degrades.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.1
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• pp.103-110
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• 2024

In this paper, a test bed for real-time network Real-Time Kinematic (RTK) research was constructed using reference stations of the NGII. A group of candidate station networks was derived, including three stations in Seoul. The group consisted of four stations with a distance of less than 100 km between them. Among several candidates, a network composed of stations with short distances between them and demonstrating good data quality for all reference stations was selected as the test bed. After collecting real-time data in Radio Technical Committee for Maritime services (RTCM) format from the selected stations and conducting a noise analysis on measurements, mm-level carrier phase measurement noise was confirmed. Afterwards, the user set the reference station inside the test bed and analyzed the network RTK positioning performance of the MAC method using the GPS L1 frequency as post-processing. From the result of the analysis it was confirmed that the residual error for all users was within 10 cm after applying the correction. Additionally, after determining integer ambiguities through Least-squares AMBiguity Decorrelation Adjustment (LAMBDA), it was confirmed that the fix rate was 100%, and all ambiguities were resolved as true values.

• Journal of Positioning, Navigation, and Timing
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• v.7 no.1
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• pp.43-52
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• 2018

In recent years, the need of high accuracy navigation for vehicles has increased due to the development of autonomous driving vehicles and increase in land transportation convenience. This study is performed for vehicle users to achieve a performance of centimeter-level positioning accuracy by utilizing Compact Network Real-time Kinematic (RTK) that is applicable as a national-level infrastructure. To this end, medium-baseline RTK was implemented in real time to estimate accurate integer ambiguities between reference stations for reliable generation of Network RTK correction using the linear combination of carrier-phase observations and L1/L2 pseudo-range measurements. The residual tropospheric error was estimated in real time to improve the accuracy of double-differenced integer ambiguity resolution between network configuration reference stations that have at least 30 km or longer baseline distance. In addition, C++ based software was developed to enable real-time generation and broadcasting of Compact Network RTK correction information by utilizing an accurately estimated double-differenced integer ambiguity values. As a result, the horizontal and vertical 95% accuracy was 2.5cm and 5.2cm, respectively, without performance degradation due to user's position change within the network.

• Journal of Advanced Navigation Technology
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• v.15 no.5
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• pp.705-713
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• 2011

In this paper, we introduce two techniques for resolving integer ambiguities between reference stations, which is one of the most important processes in Network RTK correction generation process. Each techniques uses Hatch filter and combination of L1/L2 measurements and we used simulation data and real data to evaluate performance of the techniques. For evaluating performance of each technique, we compared corrections generated from user site and Network RTK. As a result, Network RTK with the technique which uses Hatch filter improves user performance much more than single baseline RTK does. Residual of user is smaller than a half size of wavelength so it does not affect user integer ambiguity resolution, however, it contains significant bias error. On the other hand, when we used the technique which uses combination of L1/L2 measurements, residual error of user is largely reduced compared to the technique using Hatch filter.