• 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.

• 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.