LTE Signal Propagation Model-based Fingerprint DB Generation for Positioning in Emergency Rescue Situation
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다운로드
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참고문헌
키워드
초록
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 easurements, 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.
Abstract
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 easurements, 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.
참고문헌 (12)
- Bishop, C. M, "Pattern recognition and machine learning" (2006), Springer
- Cho, S. Y, "Two-step calibration for UWB-based indoor positioning system and positioning filter considering channel common bias", Measurement Science and Technology, 30 (2019)[DOI]
- 조성윤, "Positioning of Wireless Base Station using Location-Based RSRP Measurement", Journal of Positioning, Navigation, and Timing, 8(4), pp.183~192 (2019), 사단법인 항법시스템학회[DOI]
- 조성윤, "위치추정 전자지문기법을 위한 전파전달 모델 및 공간상관기법 기반의 효율적인 데이터베이스 생성", 제어.로봇.시스템학회 논문지, 20(7), pp.774~781 (2014), 제어·로봇·시스템학회[DOI]
- Cho, Y. S., "Feasibility analysis on LTE RSRP fingerprint DB estimation using sparse war-driving collecting data for emergency location", ISGNSS 2019, pp.51~58 (2019)
- Farrell, J. A., "The Global Positioning System & Inertial Navigation" (1999), McGraw-Hill
- Masoud Hamid, "Path Loss Models for LTE and LTE-A Relay Stations", Universal Journal of Communications and Network, 1(4), pp.119~126 (2013), Horizon Research Publishing Co., Ltd.[DOI]
- Kolodziej, K. W., "Local positioning systems: LBS applications and Services" (2006), Taylor and Francis Group
- B. Li, "Method for yielding a database of location fingerprints in WLAN", IEE Proceedings - Communications, 152(5), pp.580~ (2005), Institution of Engineering and Technology (IET)[DOI]
- Guan-Yi Liu, "Path Loss Measurements of Indoor LTE System for the Internet of Things", Applied Sciences, 7(6), pp.537~ (2017), MDPI AG[DOI]
- Quoc Duy Vo, "A Survey of Fingerprint-Based Outdoor Localization", IEEE Communications Surveys & Tutorials, 18(1), pp.491~506 (2016), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Alhareth Zyoud, "Parameterized indoor propagation model for mobile communication links", Microwave and Optical Technology Letters, 58(4), pp.823~826 (2016), Wiley[DOI]
