Abstract
The human’s Micro-Doppler signatures resulting from breathing, arm, foot and other periodic motion can provide valuable information about the structure of the moving parts and may be used for identification and classification purposes. In this paper, we carry out simulate with FDTD method and through wall experiment with UWB radar for human being’s periodic motion detection. In addition, Advancements signal processing methods are presented to classify and to extract the human’s periodic motion characteristic information, such as Micro-Doppler shift and motion frequency. Firstly, we apply the Principal Component Analysis (PCA) with singular value decomposition (SVD) to denoise and extract the human motion signal. Then, we present the results base on the Hilbert-Huang transform (HHT) and the S transform to classify and to identify the human’s micro-Doppler shift characteristics. The results demonstrate that the combination of UWB radar and various processing methods has potential to detect human’s Doppler signatures effectively.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
