상세정보
학술지
Human Detection Based on Time-Varying Signature on Range-Doppler Diagram Using Deep Neural Networks
- 발행일
- 202103
- 출처
- IEEE Geoscience and Remote Sensing Letters, v.18 no.3, pp.426-430
- ISSN
- 1545-598X
- 출판사
- IEEE
- 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%.
- 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)