Deep Learning-Based Indoor Distance Estimation Scheme Using FMCW Radar

• Published in: Information (Basel) Vol. 12; no. 2; p. 80
• Main Authors: Park, Kyung-Eun, Lee, Jeong-Pyo, Kim, Youngok
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-02-2021
• Subjects: Accident prevention; Accuracy; Artificial neural networks; Continuous radiation; Deep learning; distance estimation; Fast Fourier transformations; FMCW radar; Noise; positioning; Radar; Workers
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info12020080

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.