• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.270-271
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• 2015

Recently, There is Increasing interest in the intelligent services using the Internet of Things indoor positioning technologies that enable the communication of information between the objects. In particular Applications and demand for the indoor location based services using smart devices has made active. An indoor location positioning technology for this purpose BLE (Bluetooth Low Energy) has been a lot of interest in technology increases. If iBeacon of BLE(Bluetooth Low Energy) is made available to provide a signal for the indoor location information measurement then reliability of Indoor location information is lowered by signal interference. In this paper, Proposes a technique for data acquisition method for obtaining reliable position information and reliable position information calculation method from signal information data of iBeacon.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1368-1373
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• 2015

Recently, There is increasing interest in the IoT(Internet of Thing) as intelligent information service that enables communication between people and things based on internet. In particular the demand for indoor location-based services with the development of smart devices is rapidly increasing. Using the RSSI signal that is provide to the BLE(Bluetooth Low Energy) for Indoor location information measurement calculates distance information between beacon and mobile terminal. However, simply using the RSSI signal to the distance measurement has a difficult to get the indoor location information due to the influence of the signal interference and the weather. In this paper, by performing an operation for obtaining a reliable distance information from the signal information of iBeacon we propose a system for providing reliable indoor location.

• Korean Institute of Interior Design Journal
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• v.22 no.5
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• pp.207-215
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• 2013

Due to an increase in the number of large-scale and high rise buildings, the importance of indoor location information has been highlighted. As a result, seamless three-dimensional space information, linked to various indoor and outdoor services is required. The purpose of this study is to develop a system which can edit and operate indoor space information using the IndoorGML(Geography Markup Language). It provides functions such as converting and editing authoring indoor space using the IndoorGML. Based on defined schema which is the IndoorGML international standardization work, we develop the "Editor" and "Viewer" for the IndoorGML. When indoor space is modeled in an authoring tool, a variety of topologies can be created automatically. These are available to be edited and modified. Moreover, the file of model can be saved as IndoorGML, SBM and KML file. These files are viewed by the "Viewer". Indoor LBS(Location Based Service)is served with these principles.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.851-853
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• 2013

Recently, location-based service as the developed continuously, interest in positioning technology is increasing. As the most famous indoor positioning technology, WPS is a positioning technology using WiFi, which can complement the limits of the indoor positioning to have a GPS. In this paper, to provide a system for monitoring the position of the inside of the user based on the position information that using the RSSI signal of the wireless AP based WPS technology, they grip the location information of the mobile nodes in the indoor. If using the method proposed, it is expected to be applied to various services it is possible to apply the WPS, this is because it is possible to estimate in real time the location distribution of mobile nodes in the indoor.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.6
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• pp.640-651
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• 2016

We propose an effective ToA-based localization method considering accuracy in indoor environments. The purpose of the localization system is to estimate the coordinates of the geographic location of target device. In indoor environments, accurately estimating the location of a target device is not easy due to various errors. The accuracy of wireless localization is influenced by NLOS errors. ToA-based localization measures the location of a target device using the distances between a mobile device and three or more base stations. However, each of the NLOS errors along a distance estimated from a target device to a base station is different because of dissimilar obstacles. To accurately estimate the target's location, an optimized localization process is needed in indoor environments. In this paper, effective ToA-based localization method process is proposed for improving accuracy in wireless sensor networks. Performance evaluations are presented, and the experimental localization system results are proved through comparisons of various localization methods with the proposed methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.537-542
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• 2008

Cricket is an excellent indoor location system and it can successfully solve many critical problems such as user privacy, decentralized administration. But in some practical applications, Cricket sometimes didn't provide location with enough accuracy, and was unable to determine when it was giving inaccurate information. For getting high-accuracy tracking performance from location data contaminated with noise, some types of filters are required. Kalman Filter is an efficient recursive filter that estimates the state of a dynamic system from a series of incomplete and noisy measurement. The filter is very powerful in the field of autonomous and assisted navigation. In this paper, we carry out comparative studies to validate the performance of the application of Kalman Filter to Cricket based localization system.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1682-1692
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• 2021

Digital Twin, one of the core technologies of the Fourth Industrial Revolution, is attracting attention as a very suitable technology for disaster monitoring such as fires and earthquakes. In this paper, we implement a system equipped with UWB RTLS(Ultra-Wideband Real Time Location System), real-time remote control, and video streaming, which are element technologies for disaster monitoring digital twin. Since the proposed system structure is based on a cloud server, the actual location of the UWB indoor positioning-based client is transmitted to the user device in real time and stored on the cloud server for statistical and data analysis. In addition, we demonstrate through experiments that outliers occurs when the value of RSSI(Received Signal Strength Indicator) decreases due to communication collisions between UWB Tags, and propose an RSSI outlier correction algorithm to solve this problem.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.1
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• pp.49-54
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• 2021

Indoor positioning system becomes of increasing interests due to the demands for accurate indoor location information where Global Navigation Satellite System signal does not approach. Wi-Fi access points (APs) built in many construction in advance helps developing a Wi-Fi Received Signal Strength Indicator (RSSI) based indoor localization. This localization method first collects pairs of position and RSSI measurement set, which is called fingerprint database, and then estimates a user's position when given a query measurement set by comparing the fingerprint database. The challenge arises from nonlinearity and noise on Wi-Fi RSSI measurements and complexity of handling a large amount of the fingerprint data. In this paper, machine learning techniques have been applied to implement Wi-Fi based localization. However, most of existing indoor localizations focus on single position estimation. The main contribution of this paper is to develop multi-target localization by using deep neural, which is beneficial when a massive crowd requests positioning service. This paper evaluates the proposed multilocalization based on deep learning from a multi-story building, and analyses its learning effect as increasing number of target positions.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.11a
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• pp.53-54
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• 2007

• 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.