• ETRI Journal
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• v.45 no.6
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• pp.939-951
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• 2023

It is critical to estimate the location using only Long-Term Evolution (LTE) and Wi-Fi information gathered by the user's smartphone and deployable for emergency rescue, regardless of whether the Global Positioning System is received. In this research, we used a vehicle to gather LTE and Wi-Fi wireless signals over a large area for an extended period of time. After that, we used the learning technique to create a positioning database that included both collection and noncollection points. We presented a two-step positioning algorithm that utilizes coarse localization to discover a rough location in a wide area rapidly and fine localization to estimate a particular location based on the coarse position. We confirmed our technology utilizing different sorts of devices in four regional types that are generally encountered: dense urban, urban, suburban, and rural. Results presented that our algorithm can satisfactorily achieve the target accuracy necessary in emergency rescue circumstances.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.1
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• pp.62-68
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• 2016

The fingerprint, which is one of the methods of indoor localization using WiFi, has been frequently studied because of its ability to be implemented via wireless access points. This method has low positioning resolution and high computational complexity compared to other methods, caused by its dependence of reference points in the radio map. In order to compensate for these problems, this paper presents a radio map designed algorithm based on the log-distance path loss model fusing a WiFi and BLE fingerprint. The proposed algorithm designs a radio map with variable values using the log-distance path loss model and reduces distance errors using a median filter. The experimental results of the proposed algorithm, compared with existing fingerprinting methods, show that the accuracy of positioning improved by from 2.747 m to 2.112 m, and the computational complexity reduced by a minimum of 33% according to the access points.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.83-88
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• 2006

Many applications in the area of location-based services and personal navigation require nowadays the location determination of a user not only in outdoor environment but also indoor. To locate a person or object in a building, systems that use either infrared, ultrasonic or radio signals, and visible light for optical tracking have been developed. The use of WiFi for location determination has the advantage that no transmitters or receivers have to be installed in the building like in the case of infrared and ultrasonic based location systems. WiFi positioning technology adopts IEEE802.11x standard, by observing the radio signals from access points installed inside a building. These access points can be found nowadays in our daily environment, e.g. in many office buildings, public spaces and in urban areas. The principle of operation of location determination using WiFi signals is based on the measurement of the signal strengths to the surrounding available access points at a mobile terminal (e.g. PDA, notebook PC). An estimate of the location of the terminal is then obtained on the basis of these measurements and a signal propagation model inside the building. The signal propagation model can be obtained using simulations or with prior calibration measurements at known locations in an offline phase. The most common location determination approach is based on signal propagation patterns, namely WiFi fingerprinting. In this paper the underlying technology is briefly reviewed followed by an investigation of two WiFi positioning systems. Testing of the system is performed in two localization test beds, one at the Vienna University of Technology and the second at the Hong Kong Polytechnic University. First test showed that the trajectory of a moving user could be obtained with a standard deviation of about ${\pm}$ 3 m.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.1
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• pp.217-225
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• 2015

Recently, as the supply of smart phone is spreading and the exhibition industries are rapidly growing, future exhibitions are evolving toward active, customized, interactive, tangible services, which can give more satisfaction to visitors. To realize this services, the indoor localization in the exhibition of visitors is very important and according to the accuracy of localization the quality of services for exhibition are also different. In this paper, state-of-the art indoor localization techniques are investigated and new localization method are proposed. To achieve this goal, future exhibition service types are proposed, which are connected with IT technology. And also, BLE(Bluetooth Low Energy) technology including comparison of specifications and representative localization technologies are analysed. Furthermore we performed comparison study and simulation between RF and VLC channels. Finally based on this, we proposed converged VLC and RF(Wi-Fi and BLE) localization technique for exhibition.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.155-158
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• 2017

WiFi Fingerprinting기술의 측위 정확도에 가장 큰 영향을 주는 요인은 수신되는 신호세기(RSS)의 안정성이다. 하지만 실내 환경의 높은 복잡도로 인해 같은 위치에서도 RSS가 시간에 따라 변화하며 불안정하다. 이러한 RSS variance 문제를 해결 하기위한 다양한 연구들이 수행되었다. 하지만 기존 연구들의 경우 시스템의 복잡도가 증가하며, RSS가 급격히 변하는 경우에는 측위 성능을 보장 할 수 없다. 본 논문에서는 특수한 구조를 갖는 Neural Network설계하고 이에 최적화된 입력 Feature고안하며 이를 통해 급격한 RSS 변화에서도 성능을 보장하는 WiFi Fingerprinting 알고리즘 제안한다. 제안하는 알고리즘과 기존 알고리즘을 동일한 조건에서 시뮬레이션을 통해 비교한 결과 제안하는 알고리즘이 급격한 RSS 변화에서 상대적으로 높은 측위 정확도 보여줌을 확인 할 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.73-75
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• 2018

LoRaWan(Long Range Wide Area Network)은 저전력, 장거리 특성을 가진 무선 통신기술로 그 특성상 스마트 시티(Smart City), IoT(Internet of Things) 등에 각광받고 있다. 또한 LoRaWan은 Chirp 신호 특성에 의해 실외 삼각측량에 따른 사용자 위치 추정 기술을 제공한다. 본 논문에서는 이러한 LoRaWan의 특성에 더해 Wi-Fi 지문 정보를 활용하여 위치 추정 정확도를 개선하고 또한 이웃 Wi-Fi 단말들, 가령 스마트폰 등의 위치 정보를 LoraWan 게이트웨이와 통신하여 최종적으로 서버에서 측위 할 수 있는 시스템을 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5422-5441
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• 2016

Despite the abundant literature in the field, there is still the need to find a time-efficient, highly accurate, easy to deploy and robust localization algorithm for real use. The algorithm only involves minimal human intervention. We propose an enhanced Received Signal Strength Indicator (RSSI) based positioning algorithm for Wi-Fi capable devices, called the Dynamic Weighted Evolution for Location Tracking (DWELT). Due to the multiple phenomena affecting the propagation of radio signals, RSSI measurements show fluctuations that hinder the utilization of straightforward positioning mechanisms from widely known propagation loss models. Instead, DWELT uses data processing of raw RSSI values and applies a weighted posterior-probabilistic evolution for quick convergence of localization and tracking. In this paper, we present the first implementation of DWELT, intended for 1D location (applicable to tunnels or corridors), and the first step towards a more generic implementation. Simulations and experiments show an accuracy of 1m in more than 81% of the cases, and less than 2m in the 95%.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.9
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• pp.836-841
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• 2016

An indoor positioning system using Wi-Fi is essential to produce a radio map that combines the indoor space of two or more dimensions, the information of node positions, and etc. in processing for constructing the radio map, the measurement of the received signal strength indicator(RSSI) and the confirmation of node placement information counsume substantial time. Especially, when the installed wireless environment is changed or a new space is created, easy installation of the node and fast indoor radio mapping are needed to provide indoor location-based services. In this paper, to reduce the time consumption, we propose an algorithm to distinguish the straight and curve lines of a corridor section by RSSI visualization and Sobel filter-based edge detection that enable accurate node deployment and space analysis using cable-type Wi-Fi node installed at a 3 m interval. Because the cable type Wi-Fi is connected by a same power line, it has an advantage that the installation order of nodes at regular intervals could be confirmed accurately. To be able to analyze specific sections in space based on this advantage, the distribution of the signal was confirmed and analyzed by Sobel filter based edge detection and total RSSI distribution(TRD) computation through a visualization process based on the measured RSSI. As a result to compare the raw data with the performance of the proposed algorithm, the signal intensity of proposed algorithm is improved by 13.73 % in the curve section. Besides, the characteristics of the straight and the curve line were enhanced as the signal intensity of the straight line decreased by an average of 34.16 %.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.8
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• pp.1643-1654
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• 2015

With standardization of IEEE 802.11n, APs with the 2.4GHz and 5GHz dual-band capability have widely been deployed over a metropolitan area by individuals and internet service providers. Moreover, due to the increasing attentions on indoor-localization technique using Wi-Fi, the need for LOS and NLOS determination scheme is increasing to enhance accuracy of the localization. In this paper, we propose a novel LOS/NLOS determination technique by using different radio attenuation characteristics in different frequency bands and different mediums. Based on this technique, we designed a LOS/NLOS-aware indoor localization scheme. The proposed LOS/NLOS determination algorithm can be used when the distance between an user device and an AP is unknown, and the proposed localization scheme provides very accurate room-level position information. We validated the proposed scheme by implementing it on Android smart phones.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1200-1215
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• 2023

With the development of IoT and artificial intelligence, location-based services are getting more and more attention. For solving the current problem that indoor positioning error is large and generalization is poor, this paper proposes a Model Stacking Algorithm for Indoor Positioning System using WiFi fingerprinting. Firstly, we adopt a model stacking method based on Bayesian optimization to predict the location of indoor targets to improve indoor localization accuracy and model generalization. Secondly, Taking the predicted position based on model stacking as the observation value of particle filter, collaborative particle filter localization based on model stacking algorithm is realized. The experimental results show that the algorithm can control the position error within 2m, which is superior to KNN, GBDT, Xgboost, LightGBM, RF. The location accuracy of the fusion particle filter algorithm is improved by 31%, and the predicted trajectory is close to the real trajectory. The algorithm can also adapt to the application scenarios with fewer wireless access points.