• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.87-88
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• 2016

본 논문에서는 현재 서비스 중인 지자기를 이용한 위치인식 서비스를 더울 효율적이고 정확하게 이용하기 위해, 스마트폰 카메라 기능을 이용한 이미지 인식 서비스 기술을 융합하여 상호 보완적이고 완성적인 실내 위치인식 서비스 시스템을 제안하고자 한다. 본 시스템은 스마트폰 카메라와 지자기 센서를 이용하여 특정 인프라 구축 없이 데이터 분석만으로 실내 측위 시스템 구현을 목표로 한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7C
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• pp.544-553
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• 2012

A vehicle localization technology is an essential component to support many smart-vehicle applications, e.g. collision warning, adaptive cruise control, and so on. In this paper, we present a new vehicle localization algorithm based on the fusion of the sensing estimates from the local sensors and the GPS estimates from the inter-vehicle communications. The proposed algorithm consists of the greedy location data mapping algorithm and the position refinement algorithm. The former maps a sensing estimate with a GPS estimate based on the distance between themselves, and then the latter refines the GPS estimate of the subject vehicle based on the law of large numbers. From the numerical results, we demonstrate that the accuracy of the proposed algorithm outperforms that of the existing GPS estimates by at least 30 % in the longitudinal direction and by at least 60% in the lateral direction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.270-271
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• 2015

Recently, There is Increasing interest in the intelligent services using the Internet of Things indoor positioning technologies that enable the communication of information between the objects. In particular Applications and demand for the indoor location based services using smart devices has made active. An indoor location positioning technology for this purpose BLE (Bluetooth Low Energy) has been a lot of interest in technology increases. If iBeacon of BLE(Bluetooth Low Energy) is made available to provide a signal for the indoor location information measurement then reliability of Indoor location information is lowered by signal interference. In this paper, Proposes a technique for data acquisition method for obtaining reliable position information and reliable position information calculation method from signal information data of iBeacon.

• The Journal of Korean Institute of Information Technology
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• v.17 no.2
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• pp.71-77
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• 2019

With 5G standards proceeding in earnest and increasing demand for services of indoor localization, research on indoor location recognition is being studied in various industrial fields, and research based on fingerprint recognition technology using Wireless Local Area Network (WLAN) is representative. In this paper, we propose an indoor positioning system based on fingerprinting technique that uses Cloud Radio Access Network (C-RAN) architecture and Channel State Information (CSI). In order to improve the performance in indoor positioning, we combined existing fingerprinting method and K nearest neighbor (KNN) technology which is one of the machine running technique. The performance improvements of the proposed indoor positioning system was verified by comparative experiments with the existing localization technique in a indoor localizztion testbed.

• 한국공간정보시스템학회:학술대회논문집
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• 2002.11a
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• pp.75-100
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• 2002

• 한국공간정보시스템학회:학술대회논문집
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• 2005.05a
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• pp.73-78
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• 2005

최근 측위 장치와 모바일 단말 장치, 이동 통신 기술이 발달함에 따라 위치기반서비스에 대한 요구가 증가하고 있다. 그러나 위치기반서비스는 이용자에게 편리하고 유용한 정보를 제공하지만 개인의 프라이버시를 침해할 수 있는 가능성을 함께 가지고 있다. 특히 개인이 깨닫지 못하는 사이에 측위 장치가 탑재되어, 개인의 위치가 제3자에 의해 추적된다면 심각한 프라이버시 침해라 할 수 있다. 이러한 개인 위치정보를 보호하기 위해서는 법적인 규제와 기술적인 장치가 함께 마련되어야 한다. 이 논문에서는 위치정보를 보호하기 위한 위치정보보호시스템을 제시한다. 제시된 위치정보보호시스템은 법적인 규제와 표준 그리고 위치정보를 보호하기 위한 방법들이 설계 및 구현되어 있다.

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.558-563
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• 2015

There have recently been various services that use indoor location estimation technologies. Representative methods of location estimation include fingerprinting and triangulation, but they lack accuracy. Various kinds of research which apply existing location estimation methods like AOA, TOA, and TDOA are being done to solve this problem. In this paper, we study the location estimation algorithm based on AOA using a RSSI difference in indoor environments. We assume that there is a single AP with four antennas, and estimate the angle of arrival based on the RSSI value to apply the AOA algorithm. To compensate for RSSI, we use a recursive averaging filter, and use the corrected RSSI and the Pythagorean theorem to estimate the angle of arrival. The results of the experiment, show an error of 18% because of the radiation pattern of the four non-directional antennas arranged at narrow intervals.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.37 no.3
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• pp.181-189
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• 2001

This paper describes the positioning accuracy of GPS moduls (KGP9800C, KiRyung) which need to record the operating position of fishing boat as realtime processing in order to develope auto recording system of fishing boat's operating information, and then it's positioning accuracy for DGPS receiver (MGP-100D, Shin-A) and DGPS beacon (GP-36, Furuno) were analyzed. Futhermore, it was examined the positioning error of the GPS module with processing algorithm to minimize the effect of the signal fluctuations. The results indicate that the positioning error is able to decrease 5m as 2drms, and such improvement of positioning performance for GPS module (KGP9800C) can be provide the basic design data in the development of the auto recording system of fishing boat's operating information.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.75-80
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• 2021

Positioning technology is performing important functions in augmented reality, smart factory, and autonomous driving. Among the positioning techniques, the positioning method using beacons has been considered a challenging task due to the deviation of the RSSI value. In this study, the position of a moving object is predicted by training a neural network that takes the RSSI value of the receiver as an input and the distance as the target value. To do this, the measured distance versus RSSI was collected. A neural network was introduced to create synthetic data from the collected actual data. Based on this neural network, the RSSI value versus distance was predicted. The real value of RSSI was obtained as a neural network for generating synthetic data, and based on this value, the coordinates of the object were estimated by learning a neural network that tracks the location of a terminal in a virtual environment.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1882-1883
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• 2007

본 논문은 근거리무선통신을 포함한 위치서비스를 위한 무선통신응용분야에 적용될 수 있는 고신뢰 고정밀 무선위치인식기술 방법을 제안한다. 제안된 위치인식방법은 양방향통신을 통해 추정된 무선태그의 신호세기 및 시간차를 측정된 거리정보를 바탕으로 상대적 위치에 대한 확률적 계산을 수행하게 되며, 측정데이터의 부정확성으로 인한 위치측정의 오차를 줄일 수 있다. 또한 제안된 고신뢰 무선측위 코아 스택을 근거리무선네트워크 형성이 가능한 2.4 GHz ISM 협대역 밴드의 ZigBee 무선비콘 및 무선태그 디바이스들에 탑재하고 실증시험을 위하여 스마트 홈 실내 환경 아래에서 $1m\;{\times}\;1m$ 의 위치인지를 가능케 하는 위치인식시스템을 구현한다.