KEYWORDS: Radar, Education and training, Time-frequency analysis, Extremely high frequency, Doppler effect, Millimeter wave sensors, Data modeling, Radar signal processing, Radar sensor technology, Imaging systems
Continuous sign language recognition has received widespread attention in the field of human-computer interaction. Compared with optical camera based continuous sign language recognition systems, millimeter wave radar based sign language recognition systems have unique advantages such as high integration, all-weather operation, and robustness in low light environments. This study constructed a continuous Chinese sign language recognition method using 60GHz Doppler radar, which utilizes a convolutional neural network incorporating Efficient Channel Attention (ECA) to process radar micro Doppler signals. A sign language radar data acquisition device was established on the Doppler radar, and 20 commonly used sign language statements for train stations were collected in the time-frequency domain to form a dataset. Subsequently, these data were input into the proposed neural network model for recognition and classification. The experiment shows that the proposed system achieves an accuracy of 97.92% in recognizing sign language at train stations. Compared with other radar based sign language recognition systems, the proposed system exhibits higher recognition accuracy.
Sparse regularization is an effective tool for synthetic aperture radar (SAR) image despeckling. Designing effective sparse regularization terms plays a very important role in this kind of method. Existing sparse regularization despeckling methods use conventional patch-based sparse representation to design regularization term. This patch-based manner will lose some important spatial information along edges between patches, resulting in staircase effect. In this paper, we propose a new Gradient domain Convolutional Sparse Coding-based (GCSC) method for SAR image despeckling, and derive a feasible algorithm to efficiently solve the corresponding nonconvex optimization problem. In contrast to the well-known sparse regularization despeckling methods that divide a SAR image into patches and process patches individually in the spatial domain or the transform domain, GCSC works on the whole SAR image to learn a convolutional sparsifying regularizer in gradient domain. By taking advantage of the gradient domain convolutional sparse coding, GCSC can capture the correlation between local neighborhoods and exploit the gradient image global correlation to produce better edges and sharp features of SAR image. Experiments conducted on real SAR images demonstrate that the proposed GCSC outperforms those state-of-the-art SAR despeckling methods in terms of subjective and objective evaluation.
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