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A Positioning DB Generation Algorithm Applying Generative Adversarial Learning Method of Wireless Communication Signals
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- 발행일
- 202009
- 출처
- Journal of Positioning, Navigation, and Timing, v.9 no.3, pp.151-156
- ISSN
- 2288-8187
- 출판사
- 항법시스템학회
- 협약과제
- 20HS4300, 긴급구조용 측위 품질 제고를 위한 GPS 음영 지역 내 다중 신호패턴의 학습 기반 3차원 정밀측위 기술 개발, 조영수
- 초록
- 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.
- KSP 제안 키워드
- Adversarial Learning, Generation algorithm, High-density, Learning methods, Positioning method, Positioning services, Space limitation, Wide area, base station(BS), deep learning(DL), generative adversarial network