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Reference Particles-based LTE Base Station Positioning

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초록

A new positioning technique for positioning of LTE base stations is proposed. The positioning information of the base station is absolutely necessary for model-based wireless positioning, and is required in some of the various merhodologies for estimating signals in an uncorrected area when construnting a database for fingerprinting-based positioning. Using the acquired location-based Reference Signal Received Power (RSRP) information to estimate the location of the base station, it is impossible with the existing trilateration methods. Therefore, in this paper, a method using reference particles is proposed. Particles are randomly generated in the application area, and signal propagation modeling is performed assuming that a base station is located in each particle. Based on this, the errors of measurements are calculated. The particle group with the minimum measurement errors is selected, the position of the base station is estimated through weighted summation, and the signal propagation model of the corresponding base station is built at the same time. The performance of the proposed technology is verified using data acquired in Seocho-dong, Seoul.

Abstract

A new positioning technique for positioning of LTE base stations is proposed. The positioning information of the base station is absolutely necessary for model-based wireless positioning, and is required in some of the various merhodologies for estimating signals in an uncorrected area when construnting a database for fingerprinting-based positioning. Using the acquired location-based Reference Signal Received Power (RSRP) information to estimate the location of the base station, it is impossible with the existing trilateration methods. Therefore, in this paper, a method using reference particles is proposed. Particles are randomly generated in the application area, and signal propagation modeling is performed assuming that a base station is located in each particle. Based on this, the errors of measurements are calculated. The particle group with the minimum measurement errors is selected, the position of the base station is estimated through weighted summation, and the signal propagation model of the corresponding base station is built at the same time. The performance of the proposed technology is verified using data acquired in Seocho-dong, Seoul.

참고문헌 (6)

  1. 조성윤, "LTE Signal Propagation Model-based Fingerprint DB Generation for Positioning in Emergency Rescue Situation", Journal of Positioning, Navigation, and Timing, 9(3), pp.157~167 (2020), 사단법인 항법시스템학회[DOI]
  2. Diakhate, C. A. L., "Propagation channel modeling at centimeter-and-millimeter-wave frequencies in 5G urban micro-cell context", Ph.D., pp.2019
  3. Farrell, J. A., "The Global Positioning System & Inertial Navigation" (1999), McGraw-Hill
  4. Shuai Han, "Automatic Precision Control Positioning for Wireless Sensor Network", IEEE Sensors Journal, 16(7), pp.2140~2150 (2016), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
  5. Myungin Ji, "A Positioning DB Generation Algorithm Applying Generative Adversarial Learning Method of Wireless Communication Signals", Journal of Positioning, Navigation, and Timing, 9(3), pp.151~156 (2020), 사단법인 항법시스템학회[DOI]
  6. 신범주, "Performance Enhancement of Emergency Rescue System using Surface Correlation Technology", Journal of Positioning, Navigation, and Timing, 9(3), pp.183~189 (2020), 사단법인 항법시스템학회[DOI]

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