• Journal of Positioning, Navigation, and Timing
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• v.3 no.3
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• pp.123-129
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• 2014

The real-time kinematic (RTK) is one of precise positioning methods using Global Positioning System (GPS) data. In the long baseline GPS RTK, the ionospheric and tropospheric delays are critical factors for the positioning accuracy. In this paper we present RTK algorithms for long baselines more than 100 km with estimating tropospheric delays. The state vector is estimated by the extended Kalman filter. We show the experimental results of GPS RTK for various baselines (162.10, 393.37, 582.29, and 1283.57 km) by using the Korea Astronomy and Space Science Institute GPS data and one International GNSS Service (IGS) reference station located in Japan. As a result, we present that long baseline GPS RTK can provide the accurate positioning for users less than few centimeters.

• Journal of Positioning, Navigation, and Timing
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• v.7 no.3
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• pp.183-188
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• 2018

The Global Navigation Satellite System (GNSS) signal gets delayed as it goes through the troposphere before reaching the GNSS antenna. Various tropospheric models are being used to correct the tropospheric delay. In this study, we compared effectiveness of two popular troposphere correction models: Global Pressure and Temperature (GPT) and Satellite-Based Augmentation System (SBAS). One-year data from a particular site was chosen as the test case. Tropospheric delays were computed using the GPT and SBAS models and compared with the International GNSS Service tropospheric product. The bias of SBAS model computations was 3.4 cm, which is four times lower than that of the GPT model. The cause of higher biases observed in the GPT model is the fact that one cannot get wet delays from the model. If SBAS-based wet delays are added to the hydrostatic delays computed using the GPT model, then the accuracy is similar to that of the full SBAS model. From this study, one can conclude that it is better to use the SBAS model than to use the GPT model in the standard code-pseudorange data processing.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_1
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• pp.575-581
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• 2012

In general, various modeling and estimation techniques have been proposed to extract the tropospheric and ionospheric delays from the GPS CORS. In this study, Kalman filter approach is adopted to estimate the tropospheric and ionospheric delays and the proper modeling for the state vector and the variance-covariance matrix for the process noises are performed. The coordinates of reference stations and the zenith wet delays are estimated with the assumption of random walk stochastic process. Also, the first-order Gauss-Markov stochastic process is applied to compute the ionospheric effects. For the evaluation of the proposed modeling technique, Kalman filter algorithm is implemented and the numerical test is performed with the CORS data. The results show that the atmospheric effects can be estimated successfully and, as a consequence, can be used for the generation of VRS data.

• ETRI Journal
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• v.38 no.6
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• pp.1172-1178
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• 2016

The atmospheric meteorology parameters of the earth, such as temperature, pressure, and humidity, strongly influence the propagation of signals in Global Navigation Satellite Systems (GNSSs). The propagation delays associated with GNSS signals can be modeled and explained based on the atmospheric temperature, pressure, and humidity, as well as the locations of the satellites and receivers. In this paper, we propose an optimized and simplified low cost GNSS base weather station that can be used to provide a global estimate of the integrated water vapor value. Our algorithm can be used to measure the zenith tropospheric delay based on the measured propagation delays in the received signals. We also present the results of the data measurements performed at our station located in the Tlemcen region of Algeria.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.5
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• pp.421-427
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• 2013

In general, it can be assumed that the tropospheric effect are removed through double-differencing technique in short-baseline GPS data processing. This means that the high-accuracy positioning can be obtained because various error sources can be eliminated and the number of unknown can be decreased in the adjustment computation procedure. As a consequence, short-baseline data processing is widely used in the fields such as deformation monitoring which require precise positioning. However, short-baseline data processing is limited to achieve high positioning accuracy when the height difference between the reference and the rover station is significant. In this study, the effects of tropospheric delays on the determination of short-baseline is analyzed, which depends on the orientation of baseline. The GPS measurements which include tropospheric effect and measurement noises are generated by simulation, and then rover coordinates are computed by short-baseline data processing technique. The residuals of rover coordinates are analyzed to interpret the tropospheric effect on the positioning. The results show that the magnitudes of the biases in the coordinate residuals increase as the baseline length gets longer. The increasing rate is computed as 0.07cm per meter in baseline length. Therefore, the tropospheric effects should be carefully considered in short-baseline data processing when the significant height difference between the reference and rover is observed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.1
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• pp.1-7
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• 2019

In this study, we estimated the tropospheric delay of GNSS (Global Navigation Satellite System) signals during passing through the atmosphere in relation to weather and seasonal factors. For this purpose, we chose four CORS (Continuously Operating Reference Station) stations from inland (CCHJ and PYCH) and on the coast (GEOM and CHJU). A total of 48 days for each station (one set of data for each week) were downloaded from the NGII (National Geographic Information Institute) and processed it using the scientific GNSS software. The average tropospheric delays in winter are less than 2,400 mm, which is about 200 mm less than those in summer. The estimated tropospheric delay shows a similar pattern from all stations except the absolute bias in magnitude, while a large delay was observed for the station located on the coast. In addition, the delay during the day was relatively stable in winter, and the average tropospheric delay was strongly related to the orthometric height. The inland stations have tropospheric delays by the precipitation rather than humidity due to dry weather and difference in temperature. On the contrary, it was primarily caused by the humidity on the sea. The correlation between temperature and water vapor pressure is 0.9 or larger for all stations, and the tropospheric delay showed a high linear relationship with temperature. It is necessary to analyze the GNSS data with higher temporal resolution (e.g. all RINEX data of the year) to improve the stability and reliability of the correlation results.

• Journal of Astronomy and Space Sciences
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• v.26 no.2
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• pp.211-220
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• 2009

As one of the GNSS error simulation case studies, we computed tropospheric signal delays based on three well-known models (Hopfield, Modified Hopfield and Saastamoinen) and a simple model. In the computation, default meteorological values were used. The result was compared with the GIPSY result, which we assumed as truth. The RMS of a simple model with Marini mapping function was the largest, 31.0 cm. For the other models, the average RMS is 5.2 cm. In addition, to quantify the influence of the accuracy of meteorological information on the signal delay, we did sensitivity analysis of pressure and temperature. As a result, all models used this study were not very sensitive to pressure variations. Also all models, except for the modified Hopfield model, were not sensitive to temperature variations.

• Journal of Advanced Navigation Technology
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• v.15 no.4
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• pp.523-535
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• 2011

The precipitable water vapor (PW) was estimated using Global Navigation Satellite System (GNSS) from several GNSS stations within the Korean Peninsula. Nearby radiosonde sites covering the GNSS stations were used for the comparison and validation of test results. GNSS data recorded under typical and severe weather conditions were used to generalize our approach. Based on the analysis, we have confirmed that the derived PW values from the GNSS observables were well agreed on the estimates from the radiosonde observables within 10 mm level. Assuming that the GNSS observables could be a good weather monitoring tool, we further tested the performance of the current WAAS tropospheric delay model, UNB3, in the Korean Peninsula. Especially, the wet zenith delays estimated from the GNSS observables and from UNB3 delay model were compared. Test results showed that the modelled approach for the troposphere (i.e., UNB3) did not perform well especially under the wet weather conditions in the Korean Peninsula. It was suggested that a new model or a near real-time model (e.g., based on regional model from GNSS or numerical weather model) would be highly desirable for the Korean WA-DGNSS to minimize the effects of the tropospheric delay and hence to achieve high precision vertical navigation solutions.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.5
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• pp.565-574
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• 2009

The water vapor weighted vertically mean temperature(Tm) models, which were developed by the consideration of seasonal characteristics over the Korea, was used in the retrieval of precipitable water vapor (PWV) from GPS data which were observed at four GPS permanent stations. Since the weighted mean temperature relates to the water vapor pressure and temperature profile at a site, the accuracy of water vapor information which were estimated from GPS tropospheric wet delay is proportional to the accuracy of the weighted mean temperature. The adaption of Korean seasonal weighted mean temperature model, as an alternative to other formulae which are suggested from other nation, provides an improvement in the accuracy of the GPS PWV estimation. Therefore, it can be concluded that the seasonally appropriate weighted mean temperature model, which is used to convert actual zenith wet delay (ZWD) to the PWV, can be more reduced the relative biases of PWV estimated from GPS signal delays in the troposphere than other annual model, so that it would be useful for GPS PWV estimation with high accuracy.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.3
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• pp.221-235
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• 2024

Errors included in Global Navigation Satellite System (GNSS) measurements degrade the performance of user position estimation but can be mitigated by spatial correlation properties. Augmentation systems providing correction data can be broadly categorized into State Space Representation (SSR) and Observation Space Representation (OSR) methods. The satellite-based cm-level augmentation service based on the SSR broadcasts correction data via satellite signals, unlike the traditional Real-Time Kinematic (RTK) and Network RTK methods, which use OSR. To provide a large amount of correction data via the limited bandwidth of the satellite communication, efficient message structure design considering service area, correction generation, and broadcast intervals is necessary. For systematic message design, it is necessary to analyze the influence of error components included in GNSS measurements. In this study, errors in satellite orbits, satellite clocks for GPS, Galileo, BeiDou, and QZSS satellite constellations ionospheric and tropospheric delays over one year were analyzed, and their spatial decorrelations and linear modeling characteristics were examined.