Search

Abstract Radar‐based hand gesture recognition (HGR) has attracted growing interest in human–computer interaction. A rich diversity in how people perform gestures causes a large intra‐class variance, and the sample quality varies from person to person. It makes HGR more challenging to identify dynamic, complicated, and deforming hand gestures. It is urgent for the real world to explore a robust method that better identifies the gestures from non‐specified users. To address the above issues, an adaptive framework is proposed for gesture recognition, and it has two main contributions. First of all, a trajectory range Doppler map (t‐RDM) is obtained by non‐coherent accumulating for inter‐frame dependencies, and then t‐RDM is enhanced to highlight the trajectory information. Taking into account different movement patterns of the gestures, a two‐pathway convolutional neural network targeted for raw and enhanced t‐RDMs is proposed, which independently mines discriminative information from the two t‐RDMs with different salient features. Second, an adaptive individual cost (AIC) loss is proposed, aiming to establish a powerful feature representation by adaptively extracting the commonalities in variant gestures according to the sample quality. Based on a public dataset using soli radar, the proposed method is evaluated on two tasks: cross‐person recognition and cross‐scenario recognition. These two recognition modes require that the training set and the test set are mutually exclusive not only at the sample level but also at the source level. Extensive experiments demonstrate that the proposed method is superior to the existing approaches for alleviating the low recognition performance caused by gesture diversity.

As a mission-critical sensor, SAR has been applied in environmental monitoring and battlefield surveillance; moreover, SAR target recognition is one of the most important applications of SAR technology. However, in practical applications, the number of samples available for training is relatively small, so the SAR target recognition can be regarded as a small sample recognition problem. One of the main directions to solve the small sample recognition problem is to realize the data augmentation. Therefore, a SAR image data augmentation method via Generative Adversarial Nets (GAN) is proposed in this paper. The method uses Wasserstein GAN with a gradient penalty (WGAN-GP) to generate new samples based on existing SAR data, which can augment the sample number in training dataset. Meanwhile, the sample selection filters are designed to extract the generated samples with high quality and specific azimuth, which can avoid the randomness of the data augmentation, and improve the quality of the newly generated training samples. The experiments based on MSTAR data show that, for three-class recognition problem, when the training sample is only 108, the proposed method can improve the recognition rate from 79% to 91.6%; and for ten-class recognition problem, when the training sample is only 360, the proposed method can improve the recognition rate from 57.48% to 79.59%. Compared with the traditional data linear generation method, the proposed method shows significant improvement on the quantity and quality of the training samples, and can effectively solve the problem of the small sample recognition.

Although automatic target recognition (ATR) models based on data-driven algorithms have achieved excellent performance in recent years, the synthetic aperture radar (SAR) ATR model often suffered from performance degradation when it encountered a small sample set. In this paper, an integrated counterfactual sample generation and filtering approach is proposed to alleviate the negative influence of a small sample set. The proposed method consists of a generation component and a filtering component. First, the proposed generation component utilizes the overfitting characteristics of generative adversarial networks (GANs), which ensures the generation of counterfactual target samples. Second, the proposed filtering component is built by learning different recognition functions. In the proposed filtering component, multiple SVMs trained by different SAR target sample sets provide pseudo-labels to the other SVMs to improve the recognition rate. Then, the proposed approach improves the performance of the recognition model dynamically while it continuously generates counterfactual target samples. At the same time, counterfactual target samples that are beneficial to the ATR model are also filtered. Moreover, ablation experiments demonstrate the effectiveness of the various components of the proposed method. Experimental results based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) and OpenSARship dataset also show the advantages of the proposed approach. Even though the size of the constructed training set was 14.5% of the original training set, the recognition performance of the ATR model reached 91.27% with the proposed approach.