Recently, frequency-modulated continuous-wave (FMCW) radar-based hand gesture recognition (HGR) using deep learning has achieved favorable performance. However, many existing methods use extracted features separately, i.e., using one of the range, Doppler, azimuth, or elevation angle information, or a combination of any two, to train convolutional neural networks (CNNs), which ignore the interrelation among the 5-D time-varying-range-Doppler-azimuth-elevation feature space. Although there have been methods using the 5-D information, their mining of the interrelation among the 5-D feature space is not sufficient, and there is still room for improvements. This article proposes a new processing scheme of HGR based on 5-D feature cubes that are jointly encoded by a 3-D fast Fourier transform (3-D-FFT)-based method. Then, a CNN is proposed by building two novel blocks, i.e., the spatiotemporal deformable convolution (STDC) block and the adaptive spatiotemporal context-aware convolution (ASTCAC) block. Concretely, STDC is designed to cope with hand gestures' large spatiotemporal geometric transformations in the 5-D feature space. Moreover, ASTCAC is designed for modeling long-distance global relationships, e.g., relationships between pixels of the feature at the upper left corner and lower right corner, and exploring the global spatiotemporal context, in order to enhance the target feature representation and suppress interference. Finally, our presented method is verified on a large radar dataset, including 19 760 sets of 16 common hand gestures, collected by 19 subjects. Our method obtains a recognition rate of 99.53% on the validation dataset and that of 97.22% on the test dataset, which is significantly better than state-of-the-art methods.

Nowadays deep learning-based weather radar echo extrapolation methods have competently improved nowcasting quality. Current pure convolutional or convolutional recurrent neural network-based extrapolation pipelines inherently struggle in capturing both global and local spatiotemporal interactions simultaneously, thereby limiting nowcasting performances, e.g., they not only tend to underestimate heavy rainfalls' spatial coverage and intensity but also fail to precisely predict nonlinear motion patterns. Furthermore, the usually adopted pixel-wise objective functions lead to blurry predictions. To this end, we propose a novel motion-guided global-local aggregation Transformer network for effectively combining spatiotemporal cues at different time scales, thereby strengthening global-local spatiotemporal aggregation urgently required by the extrapolation task. First, we divide existing observations into both short- and long-term sequences to represent echo dynamics at different time scales. Then, to introduce reasonable motion guidance to Transformer, we customize an end-to-end module for jointly extracting motion representation of short- and long-term echo sequences (MRS, MRL), while estimating optical flow. Subsequently, based on Transformer architecture, MRS is used as queries to retrospect the most useful information from MRL for an effective aggregation of global long-term and local short-term cues. Finally, the fused feature is employed for future echo prediction. Additionally, for the blurry prediction problem, predictions from our model trained with an adversarial regularization achieve superior performances not only in nowcasting skill scores but also in precipitation details and image clarity over existing methods. Extensive experiments on two challenging radar echo datasets demonstrate the effectiveness of our proposed method.