• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.57-65
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• 2018

Conventional RF signal-based indoor localization techniques such as BLE or Wi-Fi based fingerprinting method show considerable localization errors even in small-scale indoor environments due to unstable received signal strength(RSS) of RF signals. Therefore, it is difficult to apply the existing RF-based fingerprinting techniques to large-scale indoor environments such as airports and department stores. In this paper, instead of RF signal we use the geomagnetic sensor signal for indoor localization, whose signal strength is more stable than RF RSS. Although similar geomagnetic field values exist in indoor space, an object movement would experience a unique sequence of the geomagnetic field signals as the movement continues. We use a deep neural network model called the recurrent neural network (RNN), which is effective in recognizing time-varying sequences of sensor data, to track the user's location and movement path. To evaluate the performance of the proposed geomagnetic field based indoor positioning system (IPS), we constructed a magnetic field map for a campus testbed of about $94m{\times}26$ dimension and trained RNN using various potential movement paths and their location data extracted from the magnetic field map. By adjusting various hyperparameters, we could achieve an average localization error of 1.20 meters in the testbed.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.11a
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• pp.134-136
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• 2016

최근 TV시청은 다양한 매체를 통해서 이루어지고 있으며, 특히 스마트폰을 통한 시청률이 늘고 있는 상황이다. 광고시장에서도 TV시청 중에 스마트기기를 함께 이용하는 멀티태스킹 사용자가 급증하고 있으며 특히 10~30대의 사용이 적극적이다. TV시청 중 스마트 기기의 사용분야는 메신저, 정보검색, SNS 순이며 스마트 기기사용 내용 중 69%는 시청하던 TV 시청과 관련된 것이었다. 이 중에 75%는 TV에 등장한 제품, 브랜드, 장소에 관한 것이다[1]. TV를 시청하는 상황에 스마트기기의 소셜 활동의 문자를 분석하는 것은 사용자 의도를 파악할 수 있는 의미가 있으며, 시청자의 현재 위치를 파악함으로써 시청자의 의도에 반영되는 상황을 파악할 수 있다. T-Commerce 구매 의도는 사용자의 현재 상황에 대한 순간 의도를 파악하는것이 중요하며, 이와 같은 구매의도를 파악하기 위해서 본 연구에서는 GPS와, Wi-Fi 기반 Fingerprinting 측위기법을 사용하여 특별한 도구나 장비의 설치 없이 현재위치와 멀티태스킹 데이터를 분석하여 구매의도를 파악한다. T-Commerce 소비환경 패턴이 바뀜에 따라, 다양한 소비 환경 데이터 분석은 효율적인 광고 제공과 만족도를 높일 것으로 기대된다.

• 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.

• 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.