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학술지 Human Detection Based on Time-Varying Signature on Range-Doppler Diagram Using Deep Neural Networks
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저자
김영욱, Ibrahim Alnujaim, 유성진, 정병장
발행일
202103
출처
IEEE Geoscience and Remote Sensing Letters, v.18 no.3, pp.426-430
ISSN
1545-598X
출판사
IEEE
DOI
https://dx.doi.org/10.1109/LGRS.2020.2980320
협약과제
20ZH1100, 연결의 한계를 극복하는 초연결 입체통신 기술 연구, 변우진
초록
We propose the detection of humans using millimeter-wave FMCW radar based on time-varying signatures of range-Doppler diagrams using deep recurrent neural networks (DRNNs). Demand for human detection is increasing for security, surveillance, and search and rescue purposes, recently, with a particular focus on urban areas filled with clutter and moving targets. We suggest the classification of targets based on their signatures in range-Doppler plots with time because the signatures can be consecutively observed. We measure five target types: humans, cars, cyclists, dogs, and road clutter using millimeter-wave FMCW radar that transmits fast chirps at 77 GHz. To maximize the classification accuracy using the time-varying range-Doppler signatures of the targets, we investigate and compare the performance of 2-D-deep convolutional neural networks (DCNN), 3-D-DCNN, and DRNN along with 2-D-DCNN. The DRNN plus 2-D-DCNN showed the best performance, and the classification accuracy yields 99%, with the human classification rate of 100%.
키워드
Deep convolutional neural networks (DCNN), FMCW radar, human detection, range-Doppler diagram, recurrent neural networks
KSP 제안 키워드
77 GHz, Best performance, Classification rate, Convolution neural network(CNN), Deep Recurrent Neural Networks, Deep convolutional neural networks, Deep neural network(DNN), Human classification, Human detection, Moving Target, Recurrent Neural Network(RNN)