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DecompNet: Deep Context Dependent Decomposition Network for Micro-Doppler Signature of Walking Human
IEEE Sensors Journal  (IF3.301),  Pub Date : 2021-10-04, DOI: 10.1109/jsen.2021.3117689
Mahdi Ostovan, Sadegh Samadi, Alireza Kazemi

In recent years, human activity recognition using radar micro-Doppler signature has been the focus of many researches. Echo signal from body limbs is the origin of motion parameters estimation. To this end, decomposition of this composite signal into components corresponding to each limb is necessary and rather complex. In this paper, a novel method for body micro-Doppler signal decomposition is proposed using a deep convolutional neural network designated DecompNet which uses a context window on the spectral input stream to enhance the decomposition performance. This network is able to decompose 9 components of body limbs. The proposed network, is able to perform signal decomposition and denoising simultaneously, even in low SNR conditions. So, it outperforms the other similar methods both in terms of decomposition ability and robustness to observation noise. Simulation results show that DecompNet can outperform mathematical transform-based methods and also keep working in the SNRs as low as −15dB.