• Title/Summary/Keyword: 위성항법시스템

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Accuracy Evaluation of DGPS Service via Terrestrial Digital Multimedia Broadcasting (지상파 DMB 기반 DGPS 서비스 측위 정확도 평가)

  • Kim, Hye-In;Kim, Ji-Hye;Kim, Koon-Tack;Park, Kwan-Dong;Kim, Du-Sik
    • Journal of Navigation and Port Research
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    • v.36 no.6
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    • pp.437-442
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    • 2012
  • As of 2012, for service-area-widening and commercialization of DGPS service, the Ministry of Land, Transport and Maritime Affairs has completed a DGPS service via Terrestrial Digital Multimedia Broadcasting and doing experimental broadcasting. In this study, kinematic positioning tests were conducted based on DGPS service via T-DMB using low-cost GPS equipments in a dynamic environment. Standalone GPS, single-reference NDGPS via NTRIP, and virtual-reference DGPS via T-DMB surveys were conducted at the same time. And horizontal positioning errors were computed by comparing them with the result of high-precision positioning. As a result, when the DMB transmission interval was 3 seconds, horizontal positioning errors of standalone GPS, NTRIP-DGPS, and DMB-DGPS were 2.3m, 1.0m, and 0.7m, respectively. When the interval was 1 second, horizontal positioning errors were 2.0m, 1.2m, and 0.8m, respectively. Thus horizontal positioning accuracies improved with the DMB-DGPS compared to the traditional single-reference NDGPS.

Retrieval Biases Analysis on Estimation of GNSS Precipitable Water Vapor by Tropospheric Zenith Hydrostatic Models (GNSS 가강수량 추정시 건조 지연 모델에 의한 복원 정밀도 해석)

  • Nam, JinYong;Song, DongSeob
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.37 no.4
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    • pp.233-242
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    • 2019
  • ZHD (Zenith Hydrostatic Delay) model is important parameter in estimating of GNSS (Global Navigation Satellite System) PWV (Precipitable Water Vapor) along with weighted mean temperature. The ZWD (Zenith Wet Delay) is tend to accumulate the ZHD error, so that biases from ZHD will be affected on the precision of GNSS PWV. In this paper, we compared the accuracy of GNSS PWV with radiosonde PWV using three ZHD models, such as Saastamoinen, Hopfield, and Black. Also, we adopted the KWMT (Korean Weighted Mean Temperature) model and the mean temperature which was observed by radiosonde on the retrieval processing of GNSS PWV. To this end, GNSS observation data during one year were processed to produce PWVs from a total of 5 GNSS permanent stations in Korea, and the GNSS PWVs were compared with radiosonde PWVs for the evaluating of biases. The PWV biases using mean temperature estimated by the KWMT model are smaller than radiosonde mean temperature. Also, we could confirm the result that the Saastamoinen ZHD which is most used in the GNSS meteorology is not valid in South Korea, because it cannot be exclude the possibility of biases by latitude or height of GNSS station.

Multiple Reference Network Data Processing Algorithms for High Precision of Long-Baseline Kinematic Positioning by GPS/INS Integration (GPS/INS 통합에 의한 고정밀 장기선 동적 측위를 위한 다중 기준국 네트워크 데이터 처리 알고리즘)

  • Lee, Hung-Kyu
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.29 no.1D
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    • pp.135-143
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    • 2009
  • Integrating the Global Positioning System (GPS) and Inertial Navigation System (INS) sensor technologies using the precise GPS Carrier phase measurements is a methodology that has been widely applied in those application fields requiring accurate and reliable positioning and attitude determination; ranging from 'kinematic geodesy', to mobile mapping and imaging, to precise navigation. However, such integrated system may not fulfil the demanding performance requirements when the baseline length between reference and mobil user GPS receiver is grater than a few tens of kilometers. This is because their positioning/attitude determination is still very dependent on the errors of the GPS observations, so-called "baseline dependent errors". This limitation can be remedied by the integration of GPS and INS sensors, using multiple reference stations. Hence, in order to derive the GPS distance dependent errors, this research proposes measurement processing algorithms for multiple reference stations, such as a reference station ambiguity resolution procedure using linear combination techniques, a error estimation based on Kalman filter and a error interpolation. In addition, all the algorithms are evaluated by processing real observations and results are summarized in this paper.

Physical Offset of UAVs Calibration Method for Multi-sensor Fusion (다중 센서 융합을 위한 무인항공기 물리 오프셋 검보정 방법)

  • Kim, Cheolwook;Lim, Pyeong-chae;Chi, Junhwa;Kim, Taejung;Rhee, Sooahm
    • Korean Journal of Remote Sensing
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    • v.38 no.6_1
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    • pp.1125-1139
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    • 2022
  • In an unmanned aerial vehicles (UAVs) system, a physical offset can be existed between the global positioning system/inertial measurement unit (GPS/IMU) sensor and the observation sensor such as a hyperspectral sensor, and a lidar sensor. As a result of the physical offset, a misalignment between each image can be occurred along with a flight direction. In particular, in a case of multi-sensor system, an observation sensor has to be replaced regularly to equip another observation sensor, and then, a high cost should be paid to acquire a calibration parameter. In this study, we establish a precise sensor model equation to apply for a multiple sensor in common and propose an independent physical offset estimation method. The proposed method consists of 3 steps. Firstly, we define an appropriate rotation matrix for our system, and an initial sensor model equation for direct-georeferencing. Next, an observation equation for the physical offset estimation is established by extracting a corresponding point between a ground control point and the observed data from a sensor. Finally, the physical offset is estimated based on the observed data, and the precise sensor model equation is established by applying the estimated parameters to the initial sensor model equation. 4 region's datasets(Jeon-ju, Incheon, Alaska, Norway) with a different latitude, longitude were compared to analyze the effects of the calibration parameter. We confirmed that a misalignment between images were adjusted after applying for the physical offset in the sensor model equation. An absolute position accuracy was analyzed in the Incheon dataset, compared to a ground control point. For the hyperspectral image, root mean square error (RMSE) for X, Y direction was calculated for 0.12 m, and for the point cloud, RMSE was calculated for 0.03 m. Furthermore, a relative position accuracy for a specific point between the adjusted point cloud and the hyperspectral images were also analyzed for 0.07 m, so we confirmed that a precise data mapping is available for an observation without a ground control point through the proposed estimation method, and we also confirmed a possibility of multi-sensor fusion. From this study, we expect that a flexible multi-sensor platform system can be operated through the independent parameter estimation method with an economic cost saving.