• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4B
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• pp.300-308
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• 2012

Many indoor location tracking technologies have been proposed. Generally indoor location tracking using TOA signal is used, there is a weak point that it's difficult to track the location due to obstacles like a refraction, reflection and dispersion of radio wave. In this paper, we apply ETOA(Estimated-TOA) algorithm in NLOS(Non-Line-Of-Sight) environment to solve above problem. In NLOS environment, TOA value between Beacon and Mobile node is predicted by ETOA algorithm and the tracking of indoor location is also possible to identify using two NLOS beacons of three beacons by this algorithm. We show that the proposed algorithm is accurate location tracking is accomplished using the applying the proposed algorithm to indoor moving robot and the inertia sensor of robot and Kalman filter algorithm.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.20-23
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• 2017

의료 기기 관리의 중요성에 따라 의료 기기의 실시간 실내 측위의 필요성이 대두되고 있다. 본 논문에서는 의료 기기에 Wi-Fi 태그를 부착하여 삼각 측량과 핑거프린트 방식을 이용한 의료 기기의 실시간 실내 측위 시스템을 구현하고자 한다. 중앙 제어 모듈과 의료 기기에 부착한 Wi-Fi 태그와의 통신을 통하여, 의료 기기의 위치 정보를 관리하는 데이터베이스를 실시간으로 파악함으로써 의료 기기의 정확한 위치 확인이 가능하다. 본 시스템을 통해 의료 기기에 부착한 Wi-Fi 태그의 실시간 위치 파악 및 정보 관리가 가능하여 의료 기기의 관리가 용이할 것으로 기대한다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.551-556
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• 2006

위치 정보를 얻기 위해 GPS를 이용하며 다수의 GPS 안테나를 이용하면 자세까지 구할 수 있다. 그러나 실내에서는 GPS 신호 세기가 너무 약해 동작하지 않는 단점이 있다. 본 논문에서는 GPS 대신 Zigbee와 초음파를 이용하여 실내에서도 위치와 자세를 구하는 기법을 제시하였다. Zigbee 신호와 초음파 신호의 도착 시간차로부터 송신기와 수신기간의 거리를 구할 수 있으며 이로부터 위치를 구할 수 있다. 여기에 추가의 발신기를 장착하면 두 발신기의 위치 차이로 정의되는 기저선 벡터를 구할 수 있으며 이로부터 자세를 구할 수 있다. 본 논문에서는 다수의 발신기를 이용하여 효과적으로 기저선 벡터를 구하는 기법과, 이로부터 자세를 구하는 시스템을 구축하였다. 추가로 오차해석을 통하여 구해진 자세의 정확도를 예측하였다. 실제 실험을 통하여 20cm 간격의 두 발신기를 이용하여 1도 이하의 오차를 갖는 방위각과 앙각을 연속적으로 구할 수 있음을 확인하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.786-791
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• 2021

In this paper, in order to minimize limitations such as signal interference and positioning errors in existing indoor positioning systems, a smart LED-based positioning system for excellent line-of-sight radio environments and precise location tracking is proposed to improve accuracy. An IEEE 802.4 Zigbee module is mounted on the SMPS board of a smart LED; RSSI and LQI signals are received from a moving tag, and the system is configured to transmit the measured data to the positioning server through a gateway. For the experiment, the necessary hardware, such as the gateway and the smart LED module, were separately designed, and the experiment was conducted after configuring the system in an external field office. The positioning error was within 70cm as a result of performing complex calculations in the positioning server after transmitting a vector value of the moving object obtained from the direction sensor, together with a signal from the moving object received by the smart LED. The result is a significantly improved positioning error, compared to an existing short-range wireless communications-based system, and shows the level at which commercial products can be implemented.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.766-767
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• 2015

In recent days, mart or department which have large parking lots provide service for finding parked cars using NFC and GPS technology. In case using NFC, users should directly tap NFC tag, and GPS does not work inside the parking garage. In this paper, garage parking indicator system using becoan served push notification is described. By using this system, users can automatically be aware where they parked car when they enter into the huge parking lot. This system overcomes the weakness of the existing parking indicator systems, such as using NFC tag system and using GPS system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1295-1302
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• 2010

This paper proposed indoor location recognition method based on RSSI(received signal strength indication) using the LVQ network. In order to verify the effectiveness of the proposed method, we performed experiments, and then compared to the conventional triangularity measurement method. In the experiments, we set up the system to the laboratory, divided the 40 section, and installed 6 nodes as a reference node. We obtained the log-normal path loss model of wireless channels, RSSI converted into the distance. The distance values used as the input of LVQ. To learn the LVQ network, we set the target values as section indices. In the experiments, we determined the optimal number of subclass, and confirmed that the success rate of training phase was 96%, test phase was 91%.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.7
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• pp.18-22
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• 2010

In this paper, we propose and analyze the two way ranging (TWR) positioning technique using ultra wideband (UWB) for indoor environment. We employed IEEE 802.15.3a channel as a simulation environment and used to time of arrival (TOA) algorithm. UWB technology in a low transmission power the less the effect of interfering with the existing system, the frequency can be shared so that it can improve the spectral efficiency. And, positioning technique based on UWB with a very narrow pulse width has high accuracy in indoor environment. From the simulation results, it is analyzed the probability of error and positioning performance for channel effects and confirmed usefulness of the proposed scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.11
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• pp.952-959
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• 2016

This paper proposes a novel indoor positioning system (IPS) that uses a calibrated camera sensor network and dense 3D map information. The proposed IPS information is obtained by generating a bird's-eye image from multiple camera images; thus, our proposed IPS can provide accurate position information when objects (e.g., the mobile robot or pedestrians) are detected from multiple camera views. We evaluate the proposed IPS in a real environment with moving objects in a wireless camera sensor network. The results demonstrate that the proposed IPS can provide accurate position information for moving objects. This can improve the localization performance for mobile robot operation.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.9-18
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• 2013

In this paper, we design and implement an intelligent system for finding objects with RFID(Radio Frequency IDentification) tag in which an mobile robot can do. The system we developed is a learning system of artificial neural network that uses RSSI(Received Signal Strength Indicator) value as input and absolute coordination value as target. Although a passive RFID is used for location estimation, we consider an active RFID for expansion of recognition distance. We design the proposed system and construct the environment for indoor location estimation. The designed system is implemented with software and the result related learning is shown at test bed. We show various experiment results with similar environment of real one from earning data generation to real time location estimation. The accuracy of location estimation is verified by simulating the proposed method with allowable error. We prepare local test bed for indoor experiments and build a mobile robot that can find the objects user want.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.141-146
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• 2018

Recently, location recognition technology has attracted much attention, especially for locating people or objects in an indoor environment without being influenced by the surrounding environment GPS technology is widely used as a method of recognizing the position of an object or a person. GPS is a very efficient, but it does not allow the positions of objects or people indoors to be determined. RFID is a technology that identifies the location information of a tagged object or person using radio frequency information. In this study, an RFID system is constructed and the position is measured using tags. At this time, an error occurs between the actual and measured positions. To overcome this problem, a neural network is trained using the measured and actual position data to reduce the error. In this case, since the number of read tags is not constant, they are not suitable as input values for training the neural network, so the neural network is trained by converting them into center-of-gravity inputs and median value inputs. This allows the position error to be reduce by the neural network. In addition, different numbers of trained data are used, viz. 50, 100, 200 and 300, and the correlation between the number of data input values and the error is checked. When the training is performed using the neural network, the errors of the center-of-gravity input and median value input are compared. It was found that the greater the number of trained data, the lower the error, and that the error is lower when the median value input is used than when the center-of-gravity input is used.