Deep Learning-Based Indoor Two-Dimensional Localization Scheme Using a Frequency-Modulated Continuous Wave Radar
| Autor: | Kyung-Eun Park, Jeongpyo Lee, Youngok Kim |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Scheme (programming language)
TK7800-8360 Computer Networks and Communications Computer science FMCW radar Convolutional neural network law.invention law Electronic engineering Electrical and Electronic Engineering Radar two-dimensional indoor positioning computer.programming_language Artificial neural network business.industry Deep learning deep learning Multilateration Continuous-wave radar Hardware and Architecture Control and Systems Engineering Signal Processing Continuous wave Artificial intelligence Electronics business computer |
| Zdroj: | Electronics Volume 10 Issue 17 Electronics, Vol 10, Iss 2166, p 2166 (2021) |
| ISSN: | 2079-9292 |
| DOI: | 10.3390/electronics10172166 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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