  Human motion recognition (HMR) based on wireless sensing is a low-cost
technique for scene understanding. Current HMR systems adopt support vector
machines (SVMs) and convolutional neural networks (CNNs) to classify radar
signals. However, whether a deeper learning model could improve the system
performance is currently not known. On the other hand, training a machine
learning model requires a large dataset, but data gathering from experiment is
cost-expensive and time-consuming. Although wireless channel models can be
adopted for dataset generation, current channel models are mostly designed for
communication rather than sensing. To address the above problems, this paper
proposes a deep spectrogram network (DSN) by leveraging the residual mapping
technique to enhance the HMR performance. Furthermore, a primitive based
autoregressive hybrid (PBAH) channel model is developed, which facilitates
efficient training and testing dataset generation for HMR in a virtual
environment. Experimental results demonstrate that the proposed PBAH channel
model matches the actual experimental data very well and the proposed DSN
achieves significantly smaller recognition error than that of CNN.
