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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%.
Inspec keywords: image motion analysis; recurrent neural nets; radar computing; FM radar; convolutional neural nets; image sequences; image classification; statistical analysis; radar imaging; CW radar; Doppler radar; support vector machines; feature extraction; singular value decomposition
Subjects: Support vector machines; Other topics in statistics; Algebra; Algebra; Computer vision and image processing techniques; Image recognition; Radar equipment, systems and applications; Neural nets; Other topics in statistics; Electrical engineering computing