• Journal of Positioning, Navigation, and Timing
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• 제9권3호
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• pp.151-156
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• 2020

A technology for calculating the position of a device is very important for users who receive positioning services, regardless of various indoor/outdoor or with/without any positioning infrastructure existence environments. One of the positioning resources widely used at present, LTE, is a typical infrastructure that can overcome the space limitation, however its positioning method based on the position of the LTE base station has low accuracy. A method of constructing a radio wave map of an LTE signal has been proposed as a method for overcoming the accuracy, but it takes a lot of time and cost to perform high-density collection in a wide area. In this paper, we describe a method of creating a high-density DB for the entire region by using vehicle-based partial collection data. To create a positioning database, we applied the idea of Generative Adversarial Network (GAN), which has recently been in the spotlight in the field of deep learning, and learned the collected data. Then, a virtually generated map which having the smallest error from the actual data is selected as the optimum DB. We verified the effectiveness of the positioning DB generation algorithm using the positioning data obtained from un-collected area.

• Journal of Positioning, Navigation, and Timing
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• 제9권3호
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• pp.157-167
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• 2020

Fingerprinting method is useful when estimating the location of a requestor based on LTE signals in an urban area. To do this, it is necessary to acquire location-based signals everywhere in the service area for fingerprint DB generation in advance. However, there may be signal uncollected area within a wide service area, which may cause a problem that the positioning accuracy of the requestor is low. In order to solve this problem, in this paper, signal propagation modeling is performed based on the obtained measurements, and based on this model, the signal information in the non-acquisition region is estimated. To this end, techniques for modeling signal propagation according to a method using measurements are proposed. The performance of the proposed techniques is verified based on the measurements obtained on a test bed selected as Seocho-gu, Seoul. As a result, it can be seen that signal propagation modeling performed based on multidivision segmented measurements has the most performance improvement.

• Journal of Positioning, Navigation, and Timing
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• 제10권1호
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• pp.55-66
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• 2021

In this paper, we propose a signal propagation modeling technique for generating a positioning fingerprint DB based on Long Term Evolution (LTE) signals. When a DB is created based on the location-based signal information collected in an urban area, gaps in the DB due to uncollected areas occur. The spatial interpolation method for filling the gaps has limitations. In addition, the existing gap filling technique through signal propagation modeling does not reflect the signal attenuation characteristics according to directions occurring in urban areas by considering only the signal attenuation characteristics according to distance. To solve this problem, this paper proposes a Deep Neural Network (DNN)-based signal propagation functionalization technique that considers distance and direction together. To verify the performance of this technique, an experiment was conducted in Seocho-gu, Seoul. Based on the acquired signals, signal propagation characteristics were modeled for each method, and Root Mean Squared Errors (RMSE) was calculated using the verification data to perform comparative analysis. As a result, it was shown that the proposed technique is improved by about 4.284 dBm compared to the existing signal propagation model. Through this, it can be confirmed that the DNN-based signal propagation model proposed in this paper is excellent in performance, and it is expected that the positioning performance will be improved based on the fingerprint DB generated through it.

• ETRI Journal
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• 제45권6호
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• pp.939-951
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• 2023

It is critical to estimate the location using only Long-Term Evolution (LTE) and Wi-Fi information gathered by the user's smartphone and deployable for emergency rescue, regardless of whether the Global Positioning System is received. In this research, we used a vehicle to gather LTE and Wi-Fi wireless signals over a large area for an extended period of time. After that, we used the learning technique to create a positioning database that included both collection and noncollection points. We presented a two-step positioning algorithm that utilizes coarse localization to discover a rough location in a wide area rapidly and fine localization to estimate a particular location based on the coarse position. We confirmed our technology utilizing different sorts of devices in four regional types that are generally encountered: dense urban, urban, suburban, and rural. Results presented that our algorithm can satisfactorily achieve the target accuracy necessary in emergency rescue circumstances.

• 한국측량학회지
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• 제27권2호
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• pp.195-202
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• 2009

최근 공간정보의 생성 및 활용은 전통적인 측량분야 뿐만 아니라, CNS 및 ITS 등의 민간분야로 점차 확대되고 있다. 따라서, 공간정보의 정확성과 더불어 최신성이 중요한 요소로 자리잡고 있다. 하지만 현재의 수치지도는 일괄갱신 체계로 갱신되므로 최신성을 확보할 수 없으며, 사용자의 요구사항을 충족하기가 어렵다. 따라서 본 연구에서는 효율적인 공간정보 갱신을 위해 나라장터의 입찰정보를 분석하여 지형지물 변화정보를 관리할 수 있는 관리시스템을 개발하였다. 입찰정보로부터 지형지물의 변화 가능성이 있는 공사를 자동적으로 분류하고 이를 DB화하여 지형지물 변화 예측 정보를 생성하였다. 또한 텍스트형태의 입찰정보를 공간정보 데이터와 연계한 위치정보로 변환하였다. 본 시스템이 향후 안정적으로 구현된다면 수치지도 갱신에 필요한 막대한 금액이 일부 절감될 것이며, 공간정보의 최신성 확보에 많은 기여가 예상된다.