• Journal of Internet Computing and Services
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• v.7 no.3
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• pp.31-39
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• 2006

Ubiquitous and pervasive environment presents opportunities for a rich set of location aware applications such as car navigation and intelligent robots, interactive virtual games, logistics service, asset tracking etc. Typical indoor location systems require better accuracy than what current outdoor location systems provide, Outdoor location technologies such as GPS have poor indoor performance because of the harsh nature of indoor environments, In this paper we present a novel reducing interference location system that is particularly well suited to support context aware computing. The system, called EEM (Enhance Envelop Method) alms to combine the advantages of real time tracking systems that implement distributed environment with the suitability of infrastructure sensor network.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.846-849
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• 2008

In recent years there has been growing interest in wireless sensor networks (WSNs) for a variety of indoor applications. In this paper, we present the RSSI-based localization in indoor environments. In order to evaluate the relationship between distance and RSSI, the log-normal path loss shadowing model is used. By tagging users with a sensor node and deploying a number of nodes at fixed position in the building, the RSSI can be used to determine the position of tagged user. This system operates by recording and processing signal strength information at the base stations. It combines Euclidean distance technique with signal strength matrix obtained during real-time measurement to determine the location of user. The experimental results presented the ability of this system to estimate user's location with a accuracy.

• Journal of IKEEE
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• v.27 no.4
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• pp.439-449
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• 2023

In an era marked by the increasing use of drones and the growing demand for indoor surveillance, the development of a robust application for detecting and tracking both drones and humans within indoor spaces becomes imperative. This study presents an innovative application that uses FMCW radar to detect human and drone motions from the cloud point. At the outset, the DBSCAN (Density-based Spatial Clustering of Applications with Noise) algorithm is utilized to categorize cloud points into distinct groups, each representing the objects present in the tracking area. Notably, this algorithm demonstrates remarkable efficiency, particularly in clustering drone point clouds, achieving an impressive accuracy of up to 92.8%. Subsequently, the clusters are discerned and classified into either humans or drones by employing a deep learning model. A trio of models, including Deep Neural Network (DNN), Residual Network (ResNet), and Long Short-Term Memory (LSTM), are applied, and the outcomes reveal that the ResNet model achieves the highest accuracy. It attains an impressive 98.62% accuracy for identifying drone clusters and a noteworthy 96.75% accuracy for human clusters.

• International Journal of Contents
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• v.5 no.2
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• pp.59-63
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• 2009

Since it is hard to use GPS for tracking mobile user in indoor environments, much research has focused on techniques using existing wireless local area network infrastructure. Signal strength received at a fixed location is not constant, so fingerprinting approach which use pattern matching is popular. But this approach has to pay additional costs to determine user location. This paper proposes a new approach to find user's location efficiently using an index scheme. After analyzing characteristics of RF signals, the paper suggests the data processing method how the signal strength values for each of the access points are recorded in a radio map. To reduce computational cost during the location determination phase, multidimensional indexes for radio map with the important information which is the order of the strongest access points are used.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.4
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• pp.1090-1105
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• 2012

A multi-modal sensing M2M healthcare monitoring system for the continuous monitoring of patients under their natural physiological states or elderly persons with chronic diseases is summarized. The system is designed for homecare or the monitoring of the elderly who live in country side or small rest home without enough support from caregivers or doctors, instead of patient monitoring in big hospital environment. Further insights into the natural cause and progression of diseases are afforded by context-aware sensing, which includes the use of accelerometers to monitor patient activities, or by location-aware indoor tracking based on ultrasonic and RF sensing. Moreover, indoor location tracking provides information about the location of patients in their physical environment and helps the caregiver in the provision of appropriate support.

• Journal of Advanced Navigation Technology
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• v.18 no.1
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• pp.90-94
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• 2014

This paper suggests the system that allows nearby firefighters to read about the transmission direction of a distress signal and track the location by RSSI of received distress signal which is transmitted wirelessly after sensing the distress signal of a firefighter using the Wireless Sensor Network(WSN) based on IEEE 802.15.4 and Received Signal Strength Indicator(RSSI) in the building. Also, It was confirmed that the location tracking using RSSI is possible because the difference between theoretical value and experimental value is little when experiment using the proposed system.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.67-73
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• 2008

This paper describes a system for location tracking wireless sensor nodes in an indoor environment. The sensor reading used for the location estimation is the received signal strength indication (RSSI) as given by an RF interface. By tagging users with a mobile node and deploying a number of reference nodes at fixed position in the room, the received signal strength indicator can be used to determine the position of tagged users. The system combines Euclidean distance technique with signal strength obtained by measurement driven log-normal path loss model of 2.4 GHz wireless channel. The experimental results demonstrated the ability of this system to estimate the location with a error less than 1.3m.

• Journal of Advanced Navigation Technology
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• v.17 no.6
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• pp.810-815
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• 2013

In case Wi-Fi network based First responder's position tracking system is used, range measurement must be generated from RSSI finger print database. However, it is impossible to build up finger print database and to perform rescue operation at same time in the scene of rescue. In this paper, improvised Wi-Fi network without finger print database and pedestrian dead reckoning based first responders tracking system is proposed.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.329-334
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• 2011

This paper presents the method for location estimation with obstacle tracking method. A laser scanner is used to implement the system, and we assume that the map information is known. We matches the measurement of the laser scanner to estimate the location of the robot by using sequential monte carlo (SMC) method. After estimating the robot's location, the pose of obstacles are detected and tracked, hence, we can predict the collision risk of them. Finally, we present the experiment results to verify the proposed method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.229-235
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• 2018

Research on indoor location tracking technology using smart phone is actively being carried out. Especially, in order to display the movement path of the smartphone on the map, the azimuth angle should be estimated by using the geomagnetic sensor built in most smart phones. Due to the distortion of the magnetic field due to the surrounding steel structure and the inclination of the smartphone, the estimation error of azimuthal angle may be occurred. In this paper, we propose a correction method of the geomagnetic sensor at the stationary state and a correction method for the inclination of the smartphone. We also propose a method to correct the azimuth error due to the difference between the magnetic north and the grid north.