• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.2
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• pp.390-396
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• 2015

The drastic growth of mobile communication and spreading of smart phone make the significant attention on Location Based Service. The one of most important things for vitalizations of LBS is the accurate estimating position for mobile object. Focusing on an image sensor deployed in smart phone, we develop a LED based positioning estimation framework. The developed approaches can strengthen the advantages of independent indoor applicability of LED. The estimation of LED based positioning is effectively applied to any indoor environment. We put a focus especially on the algorithmic framework. of image processing of smart phone. From LED lighting, we can obtain a typical signal image which contains the unique positioning information. Furthermore test-bed based on smart phone platform is practically developed and all data have been harvested from the actual measurement of test indoor area. This can approve the practical usefulness of proposed framework.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1881-1903
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• 2015

With the fast-developing of mobile terminals, positioning techniques based on fingerprinting method draws attention from many researchers even world famous companies. To conquer some shortcomings of the existing fingerprinting systems and further improve its performance, we propose a radio map building and updating technique, which is able to customize the spatial and temporal dependency of radio maps. The method includes indoor propagation and penetration modeling and the analysis of human traffic. Based on the combination of Ray-Tracing Algorithm, Finite-Different Time-Domain and Rough Set Theory, the approach of indoor propagation modeling accurately represents the spatial dependency of the radio map. In terms of temporal dependency, we specifically study the factor of moving people in the interest area. With measurement and statistics, the factor of human traffic is introduced as the temporal updating component. We improve our existing indoor positioning system with the proposed building and updating method, and compare the localization accuracy. The results show that the enhanced system can conquer the influence caused by moving people, and maintain the confidence probability stable during week, which enhance the actual availability and robustness of fingerprinting-based indoor positioning system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.5
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• pp.407-412
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• 2014

Concerns and demands for the Location Based Services (LBS) using Global Positioning System (GPS) and Wi-Fi are largely increased in the world in the present. In some experimental results, it was noted that many errors are frequently occurred when the distances between an anchor node and a mobile node acre measured in indoor localization environment of Wireless Personal Area Network (WPAN). In this paper, localization compensation algorithm based on maximum measurement value ($LCA_{MMV}$) for moving objects in WPAN is proposed, and the performance of the algorithm is analyzed by experiments on three scenarios for movement of mobile nodes. From the experiments, it was confirmed that the average localization accuracy of suggested algorithm was more increased than Symmetric Double-Sided Two-Way Ranging (SDS-TWR) and triangulation as average 40.9cm, 77.6cm and 6.3cm, respectively on scenario 1-3.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.603-605
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• 2021

With the expansion of the indoor service-providing robot market and the electrification of automobiles, research on autonomous driving is being actively conducted. In general, in the case of outside, the location is mainly recognized through GPS, and location positioning is performed indoors using technologies such as WiFi, UWB (Ultra-Wide Band), VLP, LiDAR, and Vision. In this paper, we introduce a system for location-positioning using LED lights with different color temperatures in an indoor environment. After installing LED lights in a simulated environment such as a tunnel, it was shown that information about the current location can be obtained through the analysis of chromaticity values according to location. Through this, it is expected to be able to obtain information about the location of the vehicle in the tunnel and the movement of the device in a room such as a warehouse or a factory.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.264-266
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• 2022

In order to apply the RNN model to the radio fingerprint-based indoor path generation technology, the data set must be continuous and sequential. However, Wi-Fi radio fingerprint data is not suitable as RNN data because continuity is not guaranteed as characteristic information about a specific location at the time of collection. Therefore, continuity information of sequential positions should be given. For this purpose, clustering is possible through classification of each region based on signal data. At this time, the continuity information between the clusters does not contain information on whether actual movement is possible due to the limitation of radio signals. Therefore, correlation information on whether movement between adjacent clusters is possible is required. In this paper, a deep learning network, a recurrent neural network (RNN) model, is used to predict the path of a moving object, and it reduces errors that may occur when predicting the path of an object by generating continuous location information for path generation in an indoor environment. We propose a method of giving correlation between clustering for generating an improved moving path that can avoid erroneous path prediction that cannot move on the predicted path.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.251-256
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• 2006

In the work package 'Integrated Positioning' of the research project NAVIO (Pedestrian Navigation Systems in Combined Indoor/Outdoor Environements) we are dealing with the navigation and guidance of visitors of our University. Thereby start points are public transport stops in the surroundings of the Vienna University of Technology and the user of the system should be guided to certain office rooms or persons. For the position determination of the user different location sensors are employed, i.e., for outdoor positioning GPS and dead reckoning sensors such as a digital compass and gyro for heading determination and accelerometers for the determination of the travelled distance as well as a barometric pressure sensor for altitude determination and for indoor areas location determination using WiFi fingerprinting. All sensors and positioning methods are combined and integrated using a Kalman filter approach. Then an optimal estimate of the current location of the user is obtained using the filter. To perform an adequate weighting of the sensors in the stochastic filter model, the sensor characteristics and their performance was investigated in several tests. The tests were performed in different environments either with free satellite visibility or in urban canyons as well as inside of buildings. The tests have shown that it is possible to determine the user's location continuously with the required precision and that the selected sensors provide a good performance and high reliability. Selected tests results and our approach will be presented in the paper.

• Korean Management Science Review
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• v.29 no.2
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• pp.79-90
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• 2012

Focusing on the rapid market penetration of smart phones, the importance of LBS (Location Based Service) is drastically increased. However, traditional GPS method has critical weakness caused by limited availability, such as indoor environment. WPS is newly attractive method as a widely applicable positioning method. In WPS, RSSI (Received Signal Strength Indication) data of all Wi-Fi APs (Access Point) are measured and stored into a huge database. The stored RSSI data in database make single radio fingerprint map. By the radio fingerprint map, we can estimate the actual position of target point. The essential factor of radio fingerprint database is data integrity of RSSI. Because of millions of APs in urban area, RSSI measurement data are seriously contaminated. Therefore, we present the unified filtering method for RSSI measurement data. As the results of filtering, we can show the effectiveness of suggested method in practical positioning system of mobile operator.

• The KIPS Transactions:PartD
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• v.19D no.3
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• pp.237-246
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• 2012

Although the development of Global Positioning System (GPS) are more and more mature, its accuracy is just acceptable for outdoor positioning, not positioning for the indoor of building and the underpass. For the positioning application area for the indoor of building and the underpass, GPS even cannot achieve that accuracy because of the construction materials while the requirement for accurate positioning in the indoor of building and the underpass, because a space, a person is necessary, may be very small space with several square meters in the indoor of building and the underpass. The Received Signal Strength (RSS) based localization is becoming a good choice especially for the indoor of building and the underpass scenarios where the WiFi signals of IEEE 802.11, Wireless LAN, are available in almost every indoor of building and the underpass. The fundamental requirement of such localization system is to estimate location from Access Point (AP) to mobile device using RSS at a specific location. The Multi-path fading effects in this process make RSS to fluctuate unpredictably, causing uncertainty in localization. To deal with this problem, the combination for the method of Neural Networks and Push-Pull Estimation is applied so that the carried along the devices can learn and make the decision of position using mobile device where it is in the indoor of building and the underpass.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.3
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• pp.183-189
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• 2020

In emergency rescue situations, the localization accuracy of the rescue requestor is a very important factor in determining the success or failure of the rescue. Indoors where Global Navigation Satellite System (GNSS) is not operated, there is no choice but to use Wi-Fi or LTE signals. However, the performance of the current emergency rescue system utilizing those RF signals is exceedingly low. In this study, the effectiveness of the surface correlation technology using the accumulated signal pattern of RF signals was verified in relation to the emergency localization technology. To validate the proposed system, we configured and tested an emergency rescue scenario in multi-floors building. When the emergency rescue was requested, it was confirmed that the initial localization error was large owing to the short length of the accumulated signal pattern. However, the localization error decreased over time, which eventually led to the accurate location information being delivered to the rescuer.

• Journal of Korea Multimedia Society
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• v.14 no.3
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• pp.433-439
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• 2011

The positioning system based on wireless AP collects AP information distributed in the real world, stores it into database, and measures the position objects by comparing with searched AP information. The existing fingerprinting method is a probabilistic modeling method that acquires much of the data collected from one location upon database composition, and stores the average of the data for the sake of use it in positioning objects. Using the average value, however, may cause the probability of errors Such errors are fatal weaknesses for services based on the accurate position. This paper described the characteristics and problems of the previously used wireless AP positioning system, and proposed a method of using the AP DB and an MSS mechanism for outdoor positioning in order to solve the aforementioned problems. And the results obtained from experimental tests showed that the proposed method achieved very low error rate(27%) compared with the existing method.