• Journal of Digital Contents Society
• /
• v.15 no.1
• /
• pp.129-136
• /
• 2014

Wi-Fi fingerprinting system is a very popular positioning method used in indoor spaces. The system depends on Wi-Fi Received Signal Strength (RSS) from Access Points (APs). However, the Wi-Fi RSS is changeable by multipath fading effect and interference due to walls, obstacles and people. Therefore, the Wi-Fi fingerprinting system produces low position accuracy. Also, Wi-Fi signals pass through walls. For this reason, the existing system cannot distinguish users' floor. To solve these problems, this paper proposes a LED fingerprinting system for accurate indoor positioning. The proposed system uses a received optical power from LEDs and LED-Identification (LED-ID) instead of the Wi-Fi RSS. In training phase, we record LED fingerprints in database at each place. In serving phase, we adopt a K-Nearest Neighbor (K-NN) algorithm for comparing existing data and new received data of users. We show that our technique performs in terms of CDF by computer simulation results. From simulation results, the proposed system shows that a positioning accuracy is improved by 8.6 % on average.

• Smart Media Journal
• /
• v.4 no.2
• /
• pp.68-76
• /
• 2015

Recently, research on indoor locating techniques using smart device sensors has been conducted actively, Owing to the exponential increase in the use of various smart devices. However, in order to develop indoor location techniques, there are limitations due to the requirement that the tracking system has to function without GPS. In this paper, we propose an accurate indoor locating system that does not require additional infrastructure. The proposed scheme is developed based on the idea that the advantages and disadvantages of "Wi-Fi Fingerprinting" and "Step Detection" techniques are complementary. In the proposed scheme, we track users with "Step Detection," and correct errors with "Wi-Fi Fingerprinting." In this paper, we demonstrate the effectiveness and feasibility of our proposed scheme through experiments.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2018.05a
• /
• pp.597-599
• /
• 2018

최근 IOT(Internet of Things)환경에서 위치기반 서비스를 제공하기 위해 실내 위치인식 연구가 활발히 진행되고 있으며 실내 위치인식 기술은 주로 WiFi, 블루투스, RFID 등으로 구현되고 있다. 본 논문은 Arduino를 이용해 WiFi 측정 및 통신이 가능한 Smart Watch를 제작하였다. 실내위치 측위를 위해 WiFi Fingerprinting기법 Radiomap을 구축한 다음 Arduino Smart Watch에서 측정한 AP신호 값을 Radiomap과 비교하여 실내위치 측정 및 데이터 수집하였다. 향후 수집된 다수의 사용자 데이터를 군집도 분석하거나 실내공간에서의 IOT(Internet of Things)분야에 활용 가능 할 것으로 예상된다.

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

• Journal of Positioning, Navigation, and Timing
• /
• v.12 no.3
• /
• pp.271-280
• /
• 2023

Pedestrian Dead Reckoning (PDR) methods using initial sensors are being studied to provide the location information of smart device users in indoor environments where satellite signals are not available. PDR can continuously estimate the location of a pedestrian regardless of the walking environment, but has the disadvantage of accumulating errors over time. Unlike this, WiFi signal-based wireless positioning technology does not accumulate errors over time, but can provide positioning information only where infrastructure is installed. It also shows different positioning performance depending on the environment. In this paper, an integrated positioning technology integrating two positioning techniques with different error characteristics is proposed. A technique for correcting the error of PDR was designed by using the location information obtained through WiFi Measurement-based fingerprinting as the measurement of Extended Kalman Filte (EKF). Here, a technique is used to variably calculate the error covariance of the filter measurements using the WiFi Fingerprinting DB and apply it to the filter. The performance of the proposed positioning technology is verified through an experiment. The error characteristics of the PDR and WiFi Fingerprinting techniques are analyzed through the experimental results. In addition, it is confirmed that the PDR error is effectively compensated by adaptively utilizing the WiFi signal to the environment through the EKF to which the adaptive error covariance proposed in this paper is applied.

• Journal of Digital Convergence
• /
• v.11 no.4
• /
• pp.243-249
• /
• 2013

In this paper, GPS(Global Positioning System) that can be used outdoors and GPS(Global Positioning System) is not available for indoor Wi-Fi(Wireless Fidelity) using the Android-based location information system has been designed and implemented. Pedestrians in a room in order to estimate the location of the pedestrian's position, regardless of need to obtain the absolute position and relative position, depending on the movement of pedestrians in a row it is necessary to estimate. In order to estimate the initial position of the pedestrian Wi-Fi Fingerprinting was used. Most existing Wi-Fi Fingerprinting position error small WKNN(Weighted K Nearest Neighbor) algorithm shortcoming EWKNN (Enhanced Weighted K Nearest Neighbor) using the algorithm raised the accuracy of the position. And in order to estimate the relative position of the pedestrian, the smart phone is mounted on the IMUInertial Measurement Unit) because the use did not require additional equipment.

• Journal of Information Technology Applications and Management
• /
• v.15 no.2
• /
• pp.51-65
• /
• 2008

The purpose of this paper is introducing WiFi based EKF(Extended Kalman Filter) method for indoor positioning. The advantages of our EKF method include: 1) Any special equipment dedicated for positioning is not required. 2) implementation of EKF does not require off-line phase of fingerprinting methods. 3) The EKF effectively minimizes squared deviation of the trilateration method. In order to experimentally prove the advantages of our method, we implemented indoor positioning systems making use of the K-NN(K Nearest Neighbors), Bayesian, decision tree, trilateration, and our EKF methods. Our experimental results show that the average-errors of K-NN, Bayesian and decision tree methods are all close to 2.4 meters whereas the average errors of trilateration and EKF are 4.07 meters and 3.528 meters, respectively. That is, the accuracy of our EKF is a bit inferior to those of fingerprinting methods. Even so, our EKF is accurate enough to be used for practical indoor LBS systems. Moreover, our EKF is easier to implement than fingerprinting methods because it does not require off-line phase.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.41 no.10
• /
• pp.1197-1209
• /
• 2016

As Indoor Positioning System demands due to increased penetration and utilization of smart device, Indoor Positioning System using Wi-Fi or BLE(Bluetooth Low Energy) beacon takes center stage. In this paper, a terminal location of the user is calculated through Microscopic Trilateration using RSSI based on BLE. In the next step, a fingerprinting map appling approximate value of Microscopic Trilateration increases an efficiency of computation amount and energy for Indoor Positioning System. I suggest Indoor Positioning Algorithm based on BLE fingerprinting considering efficiency of energy by conducting precise Trilateration that assure user's terminal position by using AP(Access Point) surrounding targeted fingerprinting cells. And This paper shows experiment and result based on An Suggesting Algorithm in comparison with a fingerprinting based on BLE and Wi-Fi that be used for Indoor Positioning System.

• Journal of Positioning, Navigation, and Timing
• /
• v.12 no.4
• /
• pp.399-408
• /
• 2023

In this paper, we propose a solution that enables continuous indoor/outdoor positioning of smartphone users through the integration of Pedestrian Dead Reckoning (PDR) and GPS/WiFi signals. Considering that accurate step detection affects the accuracy of PDR, we propose a Deep Neural Network (DNN)-based technology to distinguish between walking and non-walking signals such as walking in place. Furthermore, in order to integrate PDR with GPS and WiFi signals, a technique is used to select a proper measurement by distinguishing between indoor/outdoor environments based on GPS Dilution of Precision (DOP) information. In addition, we propose a technology to adaptively change the measurement error covariance matrix by detecting measurement outliers that mainly occur in the indoor/outdoor transition section through a residual-based χ² test. It is verified through experiments on a testbed that these technologies significantly improve the performance of PDR and PDR/GPS/WiFi fingerprinting-based integrated pedestrian navigation.