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http://dx.doi.org/10.7471/ikeee.2022.26.3.364

Design and Implementation of Human and Object Classification System Using FMCW Radar Sensor  

Sim, Yunsung (School of Electronics and Information Engineering, Korea Aerospace University)
Song, Seungjun (School of Electronics and Information Engineering, Korea Aerospace University)
Jang, Seonyoung (School of Electronics and Information Engineering, Korea Aerospace University)
Jung, Yunho (Department of Smart Air Mobility, Korea Aerospace University)
Publication Information
Journal of IKEEE / v.26, no.3, 2022 , pp. 364-372 More about this Journal
Abstract
This paper proposes the design and implementation results for human and object classification systems utilizing frequency modulated continuous wave (FMCW) radar sensor. Such a system requires the process of radar sensor signal processing for multi-target detection and the process of deep learning for the classification of human and object. Since deep learning requires such a great amount of computation and data processing, the lightweight process is utmost essential. Therefore, binary neural network (BNN) structure was adopted, operating convolution neural network (CNN) computation in a binary condition. In addition, for the real-time operation, a hardware accelerator was implemented and verified via FPGA platform. Based on performance evaluation and verified results, it is confirmed that the accuracy for multi-target classification of 90.5%, reduced memory usage by 96.87% compared to CNN and the run time of 5ms are achieved.
Keywords
BNN accelerator; embedded system; FMCW radar; FPGA; multi-target tracking;
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Times Cited By KSCI : 1  (Citation Analysis)
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