| Title |
A Deep Learning-Based Super-Resolution RF Map Reconstruction for Indoor Fingerprint Positioning |
| Authors |
임채훈(Chaehun Im) ; 정성훈(Sunghoon Jung) ; 엄차현(Chahyeon Eom) ; 이충용(Chungyong Lee) |
| DOI |
https://doi.org/10.5573/ieie.2020.57.1.9 |
| Keywords |
실내 위치 추정; 핑거프린트 측위; RF 맵 재구성; 딥러닝 |
| Abstract |
In this paper, we propose a deep learning-based radio frequency (RF) map reconstruction method to improve the accuracy of the indoor fingerprint positioning. The proposed scheme reconstructs the RF map in super-resolution using a convolutional neural network (CNN) by directly learning with sparse data and ground truth data in the offline phase. The simulation results show that the proposed method has 7.53m improved positioning accuracy than the conventional fingerprint positioning and 0.92m additional positioning accuracy than the bicubic interpolation method. |