• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.2
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• pp.237-242
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• 2009

Comparing with fixed-type Robots, Mobile Robots have the advantage of extending their workspaces. But this advantage need some sensors to detect mobile robot's position and find their goal point. This article describe the navigation teaching method of mobile robot using omni-directional position detection system. This system offers the brief position data to a processor with simple devices. In other words, when user points a goal point, this system revise the error by comparing its heading angle and position with the goal. For these processes, this system use a conic mirror and a single camera. As a result, this system reduce the image processing time to search the target for mobile robot navigation ordered by user.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.2
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• pp.37-46
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• 2016

The difference between definition time of GPS (Global Positioning System) position data and actual display time of car positions on a map could reduce the accuracy of car positions displayed in PND (Portable Navigation Device)-type CNS (Car Navigation System). Due to the time difference, the position of the car displayed on the map is not its current position, so an improved method to fix these problems is required. It is expected that a method that uses predicted future positionsto compensate for the delay caused by processing and display of the received GPS signals could mitigate these problems. Therefore, in this study an analysis was conducted to correct late processing problems of map positions by mapmatching using a Kalman filter with only GPS position data and a RRF (Road Reduction Filter) technique in a light-weight CNS. The effects on routing services are examined by analyzing differences that are decomposed into along and across the road elements relative to the direction of advancing car. The results indicate that it is possible to improve the positional accuracy in the along-the-road direction of a light-weight CNS device that uses only GPS position data, by applying a Kalman filter and RRF.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4606-4623
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• 2019

Nowadays modern cities develop in a very rapid speed. Buildings become larger than ever and the interior structures of the buildings are even more complex. This drives a high demand for precise and accurate indoor navigation systems. Although the existing commercially available 2D indoor navigation system can help users quickly find the best path to their destination, it does not intuitively guide users to their destination. In contrast, an indoor navigation system combined with augmented reality technology can efficiently guide the user to the destination in real time. Such practical applications still have various problems like position accuracy, position drift, and calculation delay, which causes errors in the navigation route and result in navigation failure. During the navigation process, the large computation load and frequent correction of the displayed paths can be a huge burden for the terminal device. Therefore, the navigation algorithm and navigation logic need to be improved in the practical applications. This paper proposes an improved navigation algorithm and navigation logic to solve the problems, creating a more accurate and effective augmented reality indoor navigation system.

• Journal of Positioning, Navigation, and Timing
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• v.6 no.1
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• pp.17-26
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• 2017

Latency occurs in RTK, where the measured position actually outputs past position when compared to the measured time. This latency has an adverse effect on the navigation accuracy. In the present study, a system that estimates the latency of RTK and compensates the position error induced by the latency was implemented. To estimate the latency, the speed obtained from an odometer and the speed calculated from the position change of RTK were used. The latency was estimated with a modified correlator where the speed from odometer is shifted by a sample until to find best fit with speed from RTK. To compensate the position error induced by the latency, the current position was calculated from the speed and heading of RTK. To evaluate the performance of the implemented method, the data obtained from an actual vehicle was applied to the implemented system. The results of the experiment showed that the latency could be estimated with an error of less than 12 ms. The minimum data acquisition time for the stable estimation of the latency was up to 55 seconds. In addition, when the position was compensated based on the estimated latency, the position error decreased by at least 53.6% compared with that before the compensation.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.173-179
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• 2022

The satellite navigation deception technology disturbs the navigation solution of the receiver by generating a deceptive signal simulating the actual satellite for the satellite navigation receiver mounted on the unmanned aerial vehicle, which is the target of deception. A single spoofing technique that creates a single deceptive position and velocity can be divided into a synchronized spoofing signal that matches the code delay, Doppler frequency, and navigation message with the real satellite and an unsynchronized spoofing signal that does not match. In order to generate a signal synchronized with a satellite signal, a very sophisticated and high precision signal generation technology is required. In addition, the current position and speed of the UAV equipped with the receiver must be accurately detected in real time. Considering the detection accuracy of the current radar technology that detects small UAVs, it is difficult to detect UAVs with an accuracy of less than one chip. In this paper, we assume the asynchrony of a single spoofing signal and propose a region defense technique using multiple spoofing signals.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.313-320
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• 2022

The light buoy installed on the sea is always flexible, because it is affected by the weather as well as passing vessels. The position of the light buoy can be cached through the AtoN AIS (Automatic Identification System) and RTU (Remote Terminal Unit). This study analyzed the position data of the light buoys for the last five years (2017-2021), as well as the distribution of the light buoys within the maximum separated position. As a result, there was a basic error of 17.9% in the position data. Additionally, the separated position error of 197 light buoys to be analyzed was 70.64%, and the AtoN RTU was worse than the AtoN AIS by equipment. On the other hand, as a result of the plotting the position data of the light buoy, it was classified into four types. The most common percussion center type, the percussion center dichotomous type in which the position is divided into two zones based on the chimney, the central movement type with a fluctuating center, and the drag type, in which the position is deviated from the center for a certain period. Except for Type-1, the type was determined according to the position at which the light buoy was installed. This study is the first to analyze the position data of the light buoy, and it is expected that it will contribute to the improvement of the quality of the position data of the light buoy.

• Journal of Navigation and Port Research
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• v.47 no.4
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• pp.231-238
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• 2023

The position of a light buoy is always flexible due to the influence of external forces such as tides and wind. The position can be checked through AIS (Automatic Identification System) or RTU (Remote Terminal Unit) for AtoN. As a result of analyzing the position data for the last five years (2017-2021) of a light buoy, the average position error was 15.4%. It is necessary to detect position error data and obtain refined position data to prevent navigation safety accidents and management. This study aimed to detect position error data and obtain refined position data by DBSCAN Clustering position data obtained through AIS or RTU for AtoN. For this purpose, 21 position data of Gunsan Port No. 1 light buoy where RTU was installed among western waters with the most position errors were DBSCAN clustered using Python library. The minPts required for DBSCAN Clustering applied the value commonly used for two-dimensional data. Epsilon was calculated and its value was applied using the k-NN (nearest neighbor) algorithm. As a result of DBSCAN Clustering, position error data that did not satisfy minPts and epsilon were detected and refined position data were acquired. This study can be used as asic data for obtaining reliable position data of a light buoy installed with AIS or RTU for AtoN. It is expected to be of great help in preventing navigation safety accidents.

• Journal of Ocean Engineering and Technology
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• v.19 no.6 s.67
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• pp.78-85
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• 2005

Initial alignment and localization are important topics in inertial navigation systems, since misalignment and initial position error wholly propagate into the navigation systems and deteriorate the performance of the systems. This paper presents the error convergence characteristics of the hybrid navigation system for underwater vehicles initial position, which is based on an inertial measurement unit (IMU) accompanying a range sensor. This paper demonstrates the improvement on the navigational performance oj the hybrid system with the range information, especially focused on the convergence of the estimation of underwater vehicles initial position error. Simulations are performed with experimental data obtained from a rotating ann test with a fish model. The convergence speed and condition of the initial error removal for random initial position errors are examined with Monte Carlo simulation. In addition, numerical simulation is conducted with an AUV model in lawn-mowing survey mode to illustrate the error convergence of the hybrid navigation System for initial position error.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.5
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• pp.1010-1018
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• 2009

An inertial navigation system for pedestrian position tracking is proposed, where the position is computed using inertial sensors mounted on shoes. Inertial navigation system(INS) errors increase with time due to inertial sensor errors, and therefore it needs to reset errors frequently. During normal walking, there is an almost periodic zero velocity instance when a foot touches the floor. Using this fact, estimation errors are reduced and this method is called the zero velocity updating algorithm. When implementing this zero velocity updating algorithm, it is important to know when is the zero velocity interval. The gait states are modeled as a Markov process and each state is estimated using the hidden Markov model smoother. With this gait estimation, the zero or nearly zero velocity interval is more accurately estimated, which helps to reduce the position estimation error.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.4
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• pp.391-398
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• 2023

In this paper, we propose a Correction Dead Reckoning (CDR) solution using correction information such as Map Matching FeedBack (MMFB) in an underground parking lot. In order to correct position errors in an underground parking lot, vehicle position and heading errors are corrected using MMFB information in road link properties. The proposed method was applied to an in-vehicle navigation system and tested. The experimental results show that the proposed robust dead reckoning solution corrects Dead Reckoning (DR) position errors that occur when driving for a long time in an underground parking lot.