Person Identification With Low Training Sample Based on Micro-Doppler Signatures Separation

Due to the ability to handle low-light environments, poor weather conditions and privacy protection, person identification based on radar micro-Doppler (m-D) signatures has emerged as a research interest. Moreover, as it is always difficult to construct large-scale radar data sets, we propose an approach to recognize a person's identity with limited training sample. In which, the fine m-D signal processing technology is first used to obtain high-quality m-D data spectrograms. A three-layer convolutional principal component analysis network with a dimension optimization architecture (CPCAN-3) is then designed to learn the highly discriminative features and address the identification problem. The model has few network parameters, is easy to train, and has low dependence on a large number of training samples. Different daily activities data are captured in an indoor environment to evaluate the performance of proposed method, and the state-of-the-art algorithms are chosen for comparison. The experimental results show that the proposed scheme performs better performance than the others on small data sets. Especially when the motion of 'running' is adopted to identify persons, the model achieves 98% accuracy on the identification of ten people.