Sparsity-based dynamic hand gesture recognition using micro-Doppler signatures

In this paper, a sparsity-driven method of micro-Doppler analysis is proposed for dynamic hand gesture recognition with radar sensor. The sparse representation of the radar signal in the time-frequency domain is achieved through the Gabor dictionary, and then the micro-Doppler features are extracted by using the orthogonal matching pursuit (OMP) algorithm and fed into classifiers for dynamic hand gesture recognition. The proposed method is validated with real data measured with a K-band radar. Experiment results show that the proposed method outperforms the principal component analysis (PCA) algorithm, with the recognition accuracy higher than 90%.