• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.239-241
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• 2011

Recently, studies on the indoor positioning system in application of wireless AP have been actively going on. The indoor wireless positioning system can be classified into several types according to the positioning techniques. Among them, the fingerprint technique is a technique that establishes the radio map by collecting MAC information of AP and RSSI (Received Signal Strength Indication) before executing positioning and then determines the position in comparison with the information of AP collected during the course of positioning. In the traditional fingerprint techniques, they control and manage by installing APs that are utilized for positioning. However, in case of specific indoors, the management can be done by installing a small number of APs but, in case of wide outdoors, it's practically impossible to install and manage equipments for positioning. In order to solve such problem, there is an improved fingerprint technique that utilizes the APs that are already scattered around. This technique will allow positioning without additional cost, but even the improved fingerprint positioning technique may incur dropped accuracy as well due to wide fluctuation of the AP information. In this paper, the traditional fingerprint technique and the improved fingerprint technique are explained in comparison, and we will compares difference in performance with the proposed WPS_WS (Wi-Fi Positioning System_Weak Signal) technique.

• Journal of Korea Multimedia Society
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• v.14 no.3
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• pp.433-439
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• 2011

The positioning system based on wireless AP collects AP information distributed in the real world, stores it into database, and measures the position objects by comparing with searched AP information. The existing fingerprinting method is a probabilistic modeling method that acquires much of the data collected from one location upon database composition, and stores the average of the data for the sake of use it in positioning objects. Using the average value, however, may cause the probability of errors Such errors are fatal weaknesses for services based on the accurate position. This paper described the characteristics and problems of the previously used wireless AP positioning system, and proposed a method of using the AP DB and an MSS mechanism for outdoor positioning in order to solve the aforementioned problems. And the results obtained from experimental tests showed that the proposed method achieved very low error rate(27%) compared with the existing method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12A
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• pp.1128-1135
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• 2010

A wireless positioning system using ultra-wideband (UWB) for indoor wireless positioning uses ranging data in order to accurately estimate location. Commonly, ranging uses time of arrival (TOA), time difference of arrival (TDOA) based on arrival time. The most fundamental issue in the ranging for wireless positioning is to obtain clock synchronization among the sensor nodes and to correct an error caused by the relative clock offset from each node. In this paper, we propose ranging and positioning technique using common clock in order to solve both clock synchronization and clock offset problems. To verify the performance of proposed, we simulated ranging and positioning in channel model introduced by IEEE 802.15.4a Task Group and then results show that location estimation is unaffected by clock offset.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.7
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• pp.9-17
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• 2014

This paper proposes a binary particle swarm optimization (BPSO) algorithm for distributed node localization in wireless sensor networks (WSNs). Each unknown node performs localization using the value of the measured distances from three or more neighboring anchors, i.e., nodes that know their location information. The node that is localized during the localization process is then used as another anchor for remaining nodes. The performances of particle swarm optimization (PSO) and BPSO in terms of localization error and computation time are compared by using simulations in Matlab. The simulation results indicate that PSO-based localization is more accurate. In contrast, BPSO algorithm performs faster for finding the location of unknown nodes for distributed localization. In addition, the effects of transmission range and number of anchor nodes on the localization error and computation time are investigated.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.101-108
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• 2019

Due to recent improvements in computer processing speed and image processing technology, researches are being actively carried out to combine information from camera with existing GNSS (Global Navigation Satellite System) and dead reckoning. In this study, developed a vision-based positioning assistant algorithm to estimate the distance to the object from stereo images. In addition, GNSS/on-board vehicle sensor/vision based positioning algorithm is developed by combining vision based positioning algorithm with existing positioning algorithm. For the performance analysis, the velocity calculated from the actual driving test was used for the navigation solution correction, simulation tests were performed to analyse the effects of velocity precision. As a result of analysis, it is confirmed that about 4% of position accuracy is improved when vision information is added compared to existing GNSS/on-board based positioning algorithm.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.132-151
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• 2022

Vehicle localization is one of the core technologies for autonomous driving. Image-based localization provides location information efficiently, and various related studies have been conducted. However, the image-based localization methods using feature points or lane information has a limitation that positioning accuracy may be greatly affected by road and driving environments. In this study, we propose a line segment matching framework for accurate vehicle localization. The proposed framework consists of four steps: line segment extraction, merging, overlap area detection, and MSLD-based segment matching. The proposed framework stably performed line segment matching at a sufficient level for vehicle positioning regardless of vehicle speed, driving method, and surrounding environment.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.1385-1388
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• 2006

위치기반서비스는 기본적으로 사용자의 위치를 파악해야 한다. 측위방법은 옥외용과 옥내용으로 구분되며, 옥외용에는 GPS 방법, 이동통신망을 이용한 방법, GPS와 이동통신망을 복합적으로 활용한 방법이 있고, 옥내용에는 적외선을 이용한 방법, 초음파를 이용한 ActiveBat과 Cricket, RF 신호를 이용한 RADAR가 있다. 기존의 옥외용 측위방법은 옥내에서는 사용이 제한되고, 기존의 옥내용 측위방법은 특수한 환경을 꾸며주어야 하는 단점이 있다. 본 논문에서는 이러한 단점을 보완한 방법으로 GPS 수신모듈과 무선LAN용 AP의 신호의 세기를 이용하여 사용자의 위치를 판단하는 옥내외 통합 측위시스템을 소개한다. 검증을 위해 제안하는 시스템을 구현해 보았으며, 실험 결과로 성능평가를 한다.

• Proceedings of the KAIS Fall Conference
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• 2011.05a
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• pp.201-203
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• 2011

본 논문은 지상에 배치된 의사위성의 배치에 따른 측위 정확도 분석에 대한 연구결과이다. 의사위성의 배치 간격에 따라 수신기에서의 측위 정확도(DOP, Dilution of Precision)가 결정된다. 본 논문에서는 다양한 배치에 따른 측위 정확도를 이론적인 연구결과를 이용하여 분석한 결과를 제시하였다. 특히 의사위성의 배치 시 디지털 지형 지도를 이용하여 정확한 위치에서 계산 될 수 있도록 하였고 측위정확도를 계산하기 위한 프로그램을 구현하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06d
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• pp.52-53
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• 2011

차세대 위치기반 서비스의 최종 형태는 실내외 구분 없이 서비스를 제공하여야 한다. 그러나 실내에서 정확한 GPS 정보를 얻기 힘들기 때문에 실내와 실외에서 서로 다른 측위방식이 사용되고 있다. 따라서 실내외 연속측위를 수행하기 위해서는 실내좌표의 값을 실외에서 사용하는 GPS 좌표로 변환할 수 있어야 한다. 본 논문에서는 실내측위 시 위치 변화에 GPS 변화율을 적용하여, 실내측위의 변화에 따라 GPS 값의 변화를 주어 실내에서도 GPS정보를 얻을 수 있는 방법을 제안한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.641-642
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• 2018

Currently, the performance of positioning technology based on radio wave fingerprint is greatly influenced by the selection of data comparison algorithm. In this case, the accuracy of the indoor positioning can be greatly improved by the data expansion technique necessary for the learning structure. In this paper, we discuss the importance of learning structure that can be applied to actual positioning through classification and extension of learning data to construct learning structure based on Wi-Fi radio fingerprint.