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http://dx.doi.org/10.12673/jant.2022.26.4.211

Design and Implementation of BNN based Human Identification and Motion Classification System Using CW Radar  

Kim, Kyeong-min (School of Electronics and Information Engineering, Korea Aerospace University)
Kim, Seong-jin (School of Electronics and Information Engineering, Korea Aerospace University)
NamKoong, Ho-jung (School of Electronics and Information Engineering, Korea Aerospace University)
Jung, Yun-ho (School of Electronics and Information Engineering, Korea Aerospace University)
Publication Information
Journal of Advanced Navigation Technology / v.26, no.4, 2022 , pp. 211-218 More about this Journal
Abstract
Continuous wave (CW) radar has the advantage of reliability and accuracy compared to other sensors such as camera and lidar. In addition, binarized neural network (BNN) has a characteristic that dramatically reduces memory usage and complexity compared to other deep learning networks. Therefore, this paper proposes binarized neural network based human identification and motion classification system using CW radar. After receiving a signal from CW radar, a spectrogram is generated through a short-time Fourier transform (STFT). Based on this spectrogram, we propose an algorithm that detects whether a person approaches a radar. Also, we designed an optimized BNN model that can support the accuracy of 90.0% for human identification and 98.3% for motion classification. In order to accelerate BNN operation, we designed BNN hardware accelerator on field programmable gate array (FPGA). The accelerator was implemented with 1,030 logics, 836 registers, and 334.904 Kbit block memory, and it was confirmed that the real-time operation was possible with a total calculation time of 6 ms from inference to transferring result.
Keywords
CW Radar; BNN; Human Identification; Motion Classification; Accelerator;
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