Human Detection Based on Time-Varying Signature on Range-Doppler Diagram Using Deep Neural Networks

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 particula...

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Veröffentlicht in:	IEEE geoscience and remote sensing letters 2021-03, Vol.18 (3), p.426-430
Hauptverfasser:	Kim, Youngwook, Alnujaim, Ibrahim, You, Sungjin, Jeong, Byung Jang
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Artificial neural networks Automobiles Classification Clutter Deep convolutional neural networks (DCNN) Detection Dogs Doppler effect Doppler sonar Feature extraction FMCW radar human detection Millimeter waves Moving targets Neural networks Radar Radar detection Radar signatures range-Doppler diagram Recurrent neural networks Search and rescue Security Urban areas
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