• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.39 no.5
• /
• pp.289-295
• /
• 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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.37 no.5
• /
• pp.397-402
• /
• 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%.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.27 no.6
• /
• pp.659-667
• /
• 2009

In this study, an integrated GNSS Simulator called Inha GNSS Simulation System (IGSS) using 3D spatial information was developed and validated. Also positioning availability and accuracy improvement were evaluated under the integrated GNSS environment using IGSS. GPS and GLONASS satellite visibility predictions were compared with real observations, and their frequency of error were 6.4% and 7.5%, respectively. To evaluate positioning availability and accuracy improvement under the integrated GNSS environment, the Daejeon government complex area was selected to be the test site because the area has high-rise buildings and thus is susceptible to signal blockages. The test consists of three parts: the first is when only GPS was used; the second is when both GPS and GLONASS were simulated; and the last is when GPS, GLONASS, and Galileo were used all together. In each case, the number of visible satellites and Dilution Of Precision were calculated and compared.

• Journal of Positioning, Navigation, and Timing
• /
• v.12 no.2
• /
• pp.149-155
• /
• 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%.

• Journal of Navigation and Port Research
• /
• v.36 no.6
• /
• pp.429-435
• /
• 2012

Although GNSS hardware and software technologies have been steadily advanced, it is still difficult to obtain reliable positioning results in the area with lots of signal blockage. In this study, algorithms for integrated GPS and GLONASS double difference relative positioning were developed and its performance was validated via simulations of signal blockages. We assumed that signal blockages are caused by high-rise buildings to the east, west, and south directions. And then, GPS-only and integrated GPS/GLONASS positioning accuracy was analysed in terms of 2-dimensional positioning accuracies. Compared with GPS-only positioning, the positioning accuracy of integrated GPS/GLONASS improved by 0.3-13.5 meters.