文章基本信息

标题：Deep Learning-Based Indoor Distance Estimation Scheme Using FMCW Radar
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作者：Kyung-Eun Park ; Jeong-Pyo Lee ; Youngok Kim 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2021
卷号：12
期号：2
页码：80
DOI：10.3390/info12020080
出版社：MDPI Publishing
摘要：In the distance estimation scheme using Frequency-Modulated-Continuous-Wave (FMCW) radar, the frequency difference, which was caused by the time delay of the received signal reflected from the target, is calculated to estimate the distance information of the target. In this paper, we propose a distance estimation scheme exploiting the deep learning technology of artificial neural network to improve the accuracy of distance estimation over the conventional Fast Fourier Transform (FFT) Max value index-based distance estimation scheme. The performance of the proposed scheme is compared with that of the conventional scheme through the experiments evaluating the accuracy of distance estimation. The average estimated distance error of the proposed scheme was 0.069 m, while that of the conventional scheme was 1.9 m.
关键词：distance estimation; deep learning; FMCW radar; positioning distance estimation ; deep learning ; FMCW radar ; positioning