• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8C
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• pp.731-740
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• 2012

To offer indoor location-aware services, the needs for efficient and accurate indoor localization system has been increased. In order to meet these requirement, we presented the BLIDx(Bidirectional Location ID exchange) protocol that is efficient localization system based on sensor network. The BLIDx protocol can cope with numerous mobile nodes simultaneously but the precision of the localization is too coarse because that uses cell based localization method. In this paper, in order to compensate for these disadvantage, we propose the fingerprint overlapping method by modifying a fingerprinting methods in WLAN, and localization system using proposed method was designed and implemented. Our experiments show that the proposed method is more accurate and robust to noise than fingerprinting method in WLAN. In this way, it was improved that low location precision of BLIDx protocol.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.456-457
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• 2021

Recently, in order to more accurately provide indoor location-based services, technologies using Wi-Fi fingerprints and deep learning are being studied. Among the deep learning models, an RNN model that can store information from the past can store continuous movements in indoor positioning, thereby reducing positioning errors. When using an RNN model for indoor positioning, the collected training data must be continuous sequential data. However, the Wi-Fi fingerprint data collected to determine specific location information cannot be used as training data for an RNN model because only RSSI for a specific location is recorded. This paper proposes a region clustering technique for sequential input data generation of RNN models based on Wi-Fi fingerprint data.

• IEMEK Journal of Embedded Systems and Applications
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• v.7 no.2
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• pp.85-93
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• 2012

In this paper, we proposed Wireless LAN (WLAN) localization method that enhances database construction based on weighting factor and analyse the characteristic of the WLAN received signals. The weighting factor plays a key role as it determines the importance of Received Signal Strength Indication (RSSI) value from number of received signals (frequency). The fingerprint method is the most widely used method in WLAN-based positioning methods because it has high location accuracy compare to other indoor positioning methods. The fingerprint method has different location accuracies which depend on training phase and positioning phase. In training phase, intensity of RSSI is measured under the various. Conventional systems adapt average of RSSI samples in a database construction, which is not quite accurate due to variety of RSSI samples. In this paper, we analyse WLAN RSSI characteristic from anechoic chamber test, and analyze the causes of various distributions of RSSI and its influence on location accuracy in indoor environments. In addition, we proposed enhanced weighting factor algorithm for accurate database construction and compare location accuracy of proposed algorithm with conventional algorithm by computer simulations and tests.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.435-447
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• 2018

Fingerprint-based biometric identification is one of the most interesting automatic systems for identifying individuals. Owing to the poor sensing environment and poor quality of skin, biometrics remains a challenging problem. The main contribution of this paper is to propose a new approach to recognizing a person's fingerprint using the fingerprint's local characteristics. The proposed approach introduces the barycenter notion applied to triangles formed by the Delaunay triangulation once the extraction of minutiae is achieved. This ensures the exact location of similar triangles generated by the Delaunay triangulation in the recognition process. The results of an experiment conducted on a challenging public database (i.e., FVC2004) show significant improvement with regard to fingerprint identification compared to simple Delaunay triangulation, and the obtained results are very encouraging.

• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.154-161
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• 2018

As the number of smartphone users increases, research on indoor location recognition service is necessary. Access to indoor locations is predominantly WiFi, Bluetooth, etc., but in most quarters, WiFi is equipped with WiFi functionality, which uses WiFi features to provide WiFi functionality. The study uses the random forest algorithm, which employs the fingerprint index of the acquired WiFi and the use of the multI-value classification method, which employs the receiver signal strength of the acquired WiFi. As the data of the fingerprint, a total of 4 radio maps using the Mac address together with the received signal strength were used. The experiment was conducted in a limited indoor space and compared to an indoor location recognition system using an existing random forest, similar to the method proposed in this study for experimental analysis. Experiments have shown that the system's positioning accuracy as suggested by this study is approximately 5.8 % higher than that of a conventional indoor location recognition system using a random forest, and that its location recognition speed is consistent and faster than that of a study.

• Journal of the Korea Society of Computer and Information
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• v.17 no.6
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• pp.173-182
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• 2012

A recent advance in smart phones is increasing utilization of location information. Existing positioning system was using GPS location for positioning. However, the GPS cannot be used indoors, if GPS location has an incorrectly problem. In order to solve indoor positioning problems of indoor location-based positioning techniques have been investigated. There are a variety of techniques based on indoor positioning techniques like as RFID, UWB, WLAN, etc. But WLAN location positioning techniques take advantage the bond in real life. WLAN indoor positioning techniques have a two kind of method that is centroid and fingerprint method. Among them, the fingerprint technique is commonly used because of the high accuracy. In order to use fingerprinting techniques make a WLAN signal map building that is need to lot of resource. In this paper, we try to solve this problem in an Indoor environment for WLAN-based fingerprint of a virtual building technique, which is proposed. Proposed technique is classified Cell environment in existed Indoor environment, all of fingerprint points are shown virtual grid map in each Cell. Its method can make fingerprint grid map very quickly using estimate virtual signal value. Also built signal value can take different value depending of the real estimate value. To solve this problem using a calibration technique for the Splite-tree is proposed. Through calibration technique that improves the accuracy for short period of time. It also is improved overall accuracy using predicted value of around position in cell.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.201-202
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• 2009

유비쿼터스 환경에서 위치추적 서비스를 구현하기 위해서는 위치 정보 및 이를 기반으로 한 주변의 상황에 대한 정보와 최적화된 위치 서비스 제공이 필요하다. 본 연구에서는 ToA방식과 Fingerprint방식의 사용되는 요소들을 결합하여 위치측정 데이터의 보정을 위한 RTLS의 기법을 연구한다.

• Journal of Electrical Engineering and Technology
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• v.7 no.5
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• pp.789-796
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• 2012

Fingerprinting is a widely used positioning technology for received signal strength (RSS) based wireless local area network (WLAN) positioning system. Though spatial RSS variation is the key factor of the positioning technology, temporal RSS variation needs to be considered for more accuracy. To deal with the spatial and temporal RSS characteristics within a unified framework, this paper proposes an extended signal propagation mode (ESPM) and a fingerprint generation method. The proposed spatiotemporal fingerprint generation method consists of two algorithms running in parallel; Kalman filtering at several measurement-sampling locations and Kriging to generate location fingerprints at dense reference locations. The two different algorithms are connected by the extended signal propagation model which describes the spatial and temporal measurement characteristics in one frame. An experiment demonstrates that the proposed method provides an improved positioning accuracy.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.3
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• pp.157-167
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• 2020

Fingerprinting method is useful when estimating the location of a requestor based on LTE signals in an urban area. To do this, it is necessary to acquire location-based signals everywhere in the service area for fingerprint DB generation in advance. However, there may be signal uncollected area within a wide service area, which may cause a problem that the positioning accuracy of the requestor is low. In order to solve this problem, in this paper, signal propagation modeling is performed based on the obtained measurements, and based on this model, the signal information in the non-acquisition region is estimated. To this end, techniques for modeling signal propagation according to a method using measurements are proposed. The performance of the proposed techniques is verified based on the measurements obtained on a test bed selected as Seocho-gu, Seoul. As a result, it can be seen that signal propagation modeling performed based on multidivision segmented measurements has the most performance improvement.

• Proceedings of the Safety Management and Science Conference
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• 2012.04a
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• pp.175-180
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• 2012

최근 GPS와 Wi-Fi가 내장된 스마트폰의 보급으로 이를 이용한 위치기반 서비스(Location-based Service)가 발전하고 있다. 하지만 위치기반 서비스들의 오차 범위는 3~4m 내외로 크기 때문에 사용자들은 더 정확한 위치를 제공하기를 바라고 있다. 본 논문에서 위치기반 서비스의 핵심기능을 담당하는 측위기술 중 흔히 사용되고 있는 삼각측량법을 활용하여 핑거프린트 방법의 측위오차를 줄일 수 있는 방안을 연구하였고 프로그램으로 구현한 후 테스트를 실시하였다. 프로그램은 Labview2010으로 구현하였고, 각각의 알고리즘을 모듈화하여 필터링 적용 전 후 및 개선효과를 비교하기 쉽도록 구성하였다. 실내 테스트는 인하대학교의 강의실에서 실시하였고 실외 테스트는 인하대학교 운동장에서 실시하였다. 테스트 결과 실내의 경우 본 논문에서 제시하는 필터링 방법을 적용하기 전의 오차가 1.02미터에서 0.83미터로 감소하였고 실외에서는 0.74미터에서 0.54미터로 오차가 감소하였다.