• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2657-2675
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• 2013

A novel neighbor selection-based fingerprinting algorithm using matrix correlation (MC) for Wi-Fi localization is presented in this paper. Compared with classic fingerprinting algorithms that usually employ a single received signal strength (RSS) sample, the presented algorithm uses multiple on-line RSS samples in the form of a matrix and measures correlations between the on-line RSS matrix and RSS matrices in the radio-map. The algorithm makes efficient use of on-line RSS information and considers RSS variations of reference points (RPs) for localization, so it offers more accurate localization results than classic neighbor selection-based algorithms. Based on the MC algorithm, an error estimation method using artificial neural network is also presented to fuse available information that includes RSS samples and localization results computed by the MC algorithm and model the nonlinear relationship between the available information and localization errors. In the on-line phase, localization errors are estimated and then used to correct the localization results to reduce negative influences caused by a static radio-map and RP distribution. Experimental results demonstrate that the MC algorithm outperforms the other neighbor selection-based algorithms and the error estimation method can reduce the mean of localization errors by nearly half.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.303-306
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• 2011

GPS 수신이 어려운 실내 환경에서 이동 단말기 사용자나 로봇의 위치를 추정하기 위해 WiFi 신호 강도를 이용하는 연구가 최근 들어 활발히 전개되고 있다. 본 논문에서는 WiFi 신호의 불안정성과 불확실성에 효과적인 가우시안 프로세서를 적용하여, 실내에서 이동 중인 스마트폰 사용자의 실시간 위치를 추정하는 방법을 제안한다. 실험을 통해 제안한 방법의 성능을 분석해보고, 성능 개선을 위한 확장 방안을 제시한다.

• 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.

• 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 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.

• 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%.

• 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.

• 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.