• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1823-1824
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• 2007

핸드폰, PDA, Laptop이 보편화 되면서 이를 이용한 위치 인식 기술의 중요성이 높아지고 있다. 이러한 위치기반 서비스(LBS : Location Based Service)는 GPS를 이용한 실외 서비스와 WLAN, Zigbee, UWB 등을 이용한 실내 서비스로 나눌 수 있다. 본 논문에서는 이미 많은 수의 기반시설(AP : Access Point)가 구축되어 있는 무선랜 기반의 효과적인 위치 측정 기법에 관한 연구를 모색 해보고자 한다. 각 AP에서 받은 신호세기(SS : Signal Strength)를 데이터 베이스에 저장한 후, 이동단말기(MU : Mobile Unit)의 위치가 요구되는 장소에서 다시 신호세기를 측정하여 데이터 베이스와 비교하여 가장 적합한 위치 데이터 정보를 리턴하는 핑거프린트(Fingerprint) 방식을 소개한다. 그리고 불안정한 신호 세기 데이터를 판별하기 위하여 단일 클래스 SVM 기법인 SVDD(Support Vector Data Description)을 이용하였다.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.37-42
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• 2023

Wi-Fi Received Signal Strength Indicator (RSSI) is considered one of the most important sensor data types for indoor localization. However, collecting a RSSI fingerprint, which consists of pairs of a RSSI measurement set and a corresponding location, is costly and time-consuming. In this paper, we propose a Wi-Fi RSSI learning technique without true location data to overcome the limitations of static database construction. Instead of the true reference positions, inertial measurement unit (IMU) data are used to generate pseudo locations, which enable a trainer to move during data collection. This improves the efficiency of data collection dramatically. From an experiment it is seen that the proposed algorithm successfully learns the unsupervised Wi-Fi RSSI positioning model, resulting in 2 m accuracy when the cumulative distribution function (CDF) is 0.8.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.119-125
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• 2018

Various techniques for indoor positioning using a smart phone have been studied. Among them, the positioning technology using the acceleration sensor and the gyro sensor built in the smartphone is widely used in conjunction with the WiFi fingerprint technology. The location tracking technology using sensors has been used for a long time, but the performance environment of the smartphone is poor and the user is moving with the smartphone in a certain posture. Therefore, in order to improve the accuracy of location tracking in a smartphone environment, it is necessary to study and develop appropriate algorithms in a mobile environment. In this paper, we analyze the performances of frequency analysis method, maximum sum of minimum acceleration method and adaptive threshold method, which are the user's moving step count detection algorithms, and determine the most accurate method.

• KIISE Transactions on Computing Practices
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• v.23 no.2
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• pp.110-115
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• 2017

We propose a new indoor positioning algorithm named Cut-off algorithm. This algorithm cuts off candidates of beacons and reference points in advance, before looking for K neighbor reference points which are guessed to be closest to the user's actual location. The algorithm consists of two phases: off-line phase, and on-line phase. In the off-line phase, RSSI and UUID data from beacons are gathered at reference points placed in the indoor environment, and construct a fingerprint map of the data. In the on-line phase, the map is reduced to a smaller one according to the RSSI data of beacons received from the user's device. The nearest K reference points are selected using the reduced map, which are used for estimating user's location. In both phases, relative ranks of the peak signals received from each beacon are used, which smoothen the fluctuations of the signals. The algorithm is shown to be more efficient in terms of accuracy and estimating time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.4
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• pp.315-325
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• 2014

In ubiquitous environment, User's location information is very important to serve personalized service to user. Previous works consider only User's locations in the big buildings and assume APs are fixed. Normal home environment, However, is consists of small spaces. And the state of APs is highly fluid. Previous research has focused on indoor localization in the building where has stationary AP environment. However, in this paper, we propose as User's Location Predicting System that finds out a space where a user is located based on Wi-Fi Fingerprint approach in home environments. The results that conducted real home environments are using the system show more than 80% accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.3
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• pp.551-560
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• 2011

Recently, There has been increasing concern about WLAN-based indoor positioning system. Most of the existing WLAN-based positioning systems use a fingerprinting method as a main approach. In the fingerprinting approach, the accuracy of the location of a mobile objects is proportional to the number of reference points. However, depending on the increasing number of reference points in the training phase, it requires more time and effort to create fingerprint database. To solve these problems, we propose the new indoor positioning algorithm that calculate the distance between a mobile objects and an AP using the information of surrounding environment WLAN based APs and applied the particle filter to the proposed algorithm in order to improve the accuracy of the estimated location in this paper. To implement this algorithm, at first environmental information database such as wall, iron door, glass door, partition etc. existing in the periphery of the AP should be established. The positioning use attenuation model and path loss model. Our experimental results with proposed algorithm are verified that the positioning accuracy was low but solved the problems with fingerprinting, compared with other positioning algorithms.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.679-685
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• 2003

The cores and deltas of fingerprints designate the parts where the flow of the ridges change radically. Observations on the change of the orientation of the ridges around the cores and deltas enable us to guess the location of the cores and deltas. According]y clustering the orientation flowing to the same direction after doing research on the orientation of the ridges on the whole makes us see that the cores and deltas are shaping around the boundaries of the clustering area. It is also observed that The patterns of clustering of the orientation of the ridges classified as Arch, Tented Arch, Left loop, Right Loop and Whorl have its own characteristics respectively. In this paper the method of classifying the fingerprints effectively is proposed and proved its effectiveness by using the clustering of the orientation of the ridges, finding the cores of the fingerprints which don't secure the deltas.

• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.64-69
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• 2013

Recently, users who are interested in the service at indoor spaces is increasing. An indoor positioning system can minimize a range of positioning error using a variety of wireless communication infrastructure. Also, the system improves an indoor positioning accuracy by combining a mobile communication network. However, flexible positioning technologies regardless of an environment are insufficient. Therefore, this is time for a systematic study on an indoor positioning system business model. This paper classify differences between an indoor positioning system technology and outdoor positioning system technology. And we research a construction and application of the indoor positioning system that is adapted a wireless communication system (Wi-Fi, Bluetooth, RFID, UWE, Fingerprint, etc.) in domestic and foreign. We present a successful model of indoor positioning system and the development for future systems.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.83-85
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• 2016

위치측위 시스템들이 발전함에 따라 실내에서도 다양한 방법으로 신뢰성이 높은 실내 위치측위 시스템들이 보편화되고 있는 추세이다. 허나 기술의 한계점으로 지구자기장을 활용한 새로운 기술이 연구되고 있음에도 불구하고, 위 기술 또한 기존 기술들과는 다른 한계점이 존재한다. 이에 따라 본 논문에서는 각각의 기술점의 한계점을 해소하기 위해서 기존 실내측위 시스템과 새롭게 개발 중인 실내 측위 시스템을 혼합하여 외부장비의 의존도를 낮추고 신뢰성 높은 위치측위를 지속적으로 제공할수 있는 혼합형 실내 위치측위 시스템을 제안하고자 한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.2
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• pp.119-126
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• 2011

In this paper, we expect that the IEEE 802.15.4a, which is based on CSS, will be used a lot without getting help from other systems or sensors and will make it possible to measure the distance between radio chips in sensor network field, where the location information of the standard have to be based upon. But, the error rate will be high, so we will correct the location of the tag, which will be received by anchor. The technology of location correction we offer is reducing the error rate through calculating the distance from Compensation Tag, and after that, unite the Toa method with the Fingerprint method and adapt them to location correction technology, calculate the location's estimate, and finally abstract the best suited location estimate for Compensation Tag. At last, we offer developing systems as indoor systems of CSS, which pursue the location between nodes, and a thesis about indoor systems and making their accuracy higher.