Indoor Positioning Using RFID Technique

윤창선,; 김태인,; 김현진,; 홍연찬,

doi:10.6109/jkiice.2016.20.1.207

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…Because a global navigation satellite system is not available indoors, various indoor localization systems using radio signals, such as Wi-Fi, Zigbee, RFID, Bluetooth, ultra-wide bandwidth (UWB) radar, and frequency-modulated continuous wave (FMCW) radar, have been introduced [5][6][7][8][9][10][11]. The multilateration method and the fingerprint method are well known as localization techniques that use radio signals such as Wi-Fi, Zigbee, RFID, and Bluetooth.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Indoor Two-Dimensional Localization Scheme Using a Frequency-Modulated Continuous Wave Radar

2021

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In this paper, we propose a deep learning-based indoor two-dimensional (2D) localization scheme using a 24 GHz frequency-modulated continuous wave (FMCW) radar. In the proposed scheme, deep neural network and convolutional neural network (CNN) models that use different numbers of FMCW radars were employed to overcome the limitations of the conventional 2D localization scheme that is based on multilateration methods. The performance of the proposed scheme was evaluated experimentally and compared with the conventional scheme under the same conditions. According to the results, the 2D location of the target could be estimated with a proposed single radar scheme, whereas two FMCW radars were required by the conventional scheme. Furthermore, the proposed CNN scheme with two FMCW radars produced an average localization error of 0.23 m, while the error of the conventional scheme with two FMCW radars was 0.53 m.

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