Human Motion Classification with Micro-Doppler Radar and Bayesian-Optimized Convolutional Neural Networks
In recent years, Doppler radar has emerged as an alternative sensing modality for human gait classification since it measures not only the target speed, but also the local dynamics of the moving body parts, thereby creating a unique spectral signature. This paper presents a learning-based method for classifying human motions from micro-Doppler signals. Inspired by the applications of deep learning, the proposed method extracts features from the time-frequency representation of the radar signal using a cascaded of convolutional network layers. To design a optimal network architecture, the Bayesian optimization with Gaussian process priors is employed. Experimental results on real data are presented, which show a significant improvement compared to three existing approaches.