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- W3208546876 abstract "Since FMCW radar measures the range, the Doppler and the angle of the targets frame by frame in time sequences, invariant micro-Doppler features become a tough challenge for deep learning based classification and recognition in radar signal processing and artificial intelligence communities. This paper presents an approach to create micro-Doppler images from range-Doppler images sequences by sum of Doppler profiles in targeted range bins. The time invariant micro-Doppler features are extracted by statistical characteristics and motion pattern of the human activities. The statistical features reflect the distribution of the micro-Doppler and the energy property of movements for different human activity classes. The motion pattern features are decomposed by Singular Value Decomposition (SVD). Furthermore, the micro-Doppler features are analyzed to find significance by Support Vector Machine (SVM). Based on these micro-Doppler significance, Faster RCNN is trained and used for detection of human activities. Experimental results show that the proposed micro-Doppler features can distinguish the human motion pattern very well and the accuracy of the Faster RCNN based human activity classification achieves more than 95%." @default.
- W3208546876 created "2021-11-08" @default.
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- W3208546876 date "2021-01-01" @default.
- W3208546876 modified "2023-09-23" @default.
- W3208546876 title "Micro-doppler feature and image based human activity classification with FMCW radar" @default.
- W3208546876 doi "https://doi.org/10.1049/icp.2021.0555" @default.
- W3208546876 hasPublicationYear "2021" @default.
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