• 한국측량학회지
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• 제39권5호
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• pp.289-295
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• 2021

Recently, owing to the development of autonomous vehicles, research on precisely determining the position of a moving object has been actively conducted. Previous research mainly used the fusion of GNSS/IMU (Global Positioning System / Inertial Navigation System) and sensors attached to the vehicle through a Kalman filter. However, in recent years, new technologies have been used to determine the location of a moving object owing to the improvement in computing power and the advent of deep learning. Various techniques using RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory), and NARX (Nonlinear Auto-Regressive eXogenous model) exist for such learning-based positioning methods. The purpose of this study is to compare the precision of existing filter-based sensor fusion technology and the NARX-based method in case of GNSS signal blockages using simulation data. When the filter-based sensor integration technology was used, an average horizontal position error of 112.8 m occurred during 60 seconds of GNSS signal outages. The same experiment was performed 100 times using the NARX. Among them, an improvement in precision was confirmed in approximately 20% of the experimental results. The horizontal position accuracy was 22.65 m, which was confirmed to be better than that of the filter-based fusion technique.

• 한국측량학회지
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• 제37권5호
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• pp.397-402
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• 2019

Recently, with the emergence of autonomous vehicles and the increasing interest in safety, a variety of research has been being actively conducted to precisely estimate the position of a vehicle by fusing sensors. Previously, researches were conducted to determine the location of moving objects using GNSS (Global Navigation Satellite Systems) and/or IMU (Inertial Measurement Unit). However, precise positioning of a moving vehicle has lately been performed by fusing data obtained from various sensors, such as LiDAR (Light Detection and Ranging), on-board vehicle sensors, and cameras. This study is designed to enhance kinematic vehicle positioning performance by using feature-based recognition. Therefore, an analysis of the required precision of the observations obtained from the images has carried out in this study. Velocity and attitude observations, which are assumed to be obtained from images, were generated by simulation. Various magnitudes of errors were added to the generated velocities and attitudes. By applying these observations to the positioning algorithm, the effects of the additional velocity and attitude information on positioning accuracy in GNSS signal blockages were analyzed based on Kalman filter. The results have shown that yaw information with a precision smaller than 0.5 degrees should be used to improve existing positioning algorithms by more than 10%.

• 한국측량학회지
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• 제27권6호
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• pp.659-667
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• 2009

이 연구에서는 3차원 건물좌표를 이용한 통합 GNSS 시뮬레이터 IGSS(Inha GNSS Simulation System)를 개발하고 정확도를 검증하였다. 또한 IGSS를 이용하여 통합 GNSS 환경에서의 측위 가용성 및 정확도 향상을 평가하였다. GPS와 GLONASS의 예측결과를 실제관측 결과와 비교하여 시뮬레이터를 검증하였으며 그 결과, GPS와 GLONASS의 오차 발생빈도가 각각 6.4%와 7.5%로 나타났다. 통합 GNSS 환경에서의 측위 가용성과 정확도 향상에 관한 평가는 중고층 건물에 의한 신호차폐현상이 심한 정부대전청사를 대상으로 하였다. GPS를 단독으로 사용하였을 경우, GPS와 GLONASS를 동시에 사용하였을 경우, GPS, GLONASS, 그리고 Galileo를 함께 사용하였을 경우를 구분하여 가시위성의 개수와 정밀도 저하율을 산출하고 그 결과를 비교 및 평가하였다.

• Journal of Positioning, Navigation, and Timing
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• 제12권2호
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• pp.149-155
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• 2023

In urban areas it can be difficult to utilize global navigation satellite systems (GNSS) due to signal reflections and blockages. It is thus crucial to detect reflected or blocked signals because they lead to significant degradation of GNSS positioning accuracy. In a previous study, a classifier for global positioning system (GPS) signal reception conditions was developed using three features and the support vector machine (SVM) algorithm. However, this classifier had limitations in its classification performance. Therefore, in this study, we developed an improved machine learning based method of classifying GPS signal reception conditions by including an additional feature with the existing features. Furthermore, we applied various machine learning classification algorithms. As a result, when tested with datasets collected in different environments than the training environment, the classification accuracy improved by nine percentage points compared to the existing method, reaching up to 58%.

• 한국항해항만학회지
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• 제36권6호
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• pp.429-435
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• 2012

위성항법시스템의 기술발전이 지속적으로 진행되고 있지만 여전히 신호차폐가 빈번히 발생하는 지역에서는 측위정확도 확보에 어려움을 겪고 있다. 이 연구에서는 통합 GPS/GLONASS 이중차분 상대측위 알고리즘을 구현하고 신호차폐 환경의 시뮬레이션을 수행해 그 성능을 검증하였다. 동쪽, 서쪽, 남쪽 방향으로 고층건물에 의해 신호차폐가 발생하는 환경을 시뮬레이션 하고 시뮬레이션 상황에 따른 GPS와 GPS/GLONASS의 정확도 평가를 수행하였다. 그 결과, 신호차폐 시뮬레이션 환경에서는 GPS/GLONASS가 GPS에 비해 최소 0.3m에서 최대 13m이상의 수평정확도가 향상되었다.