High Resolution Range Profile (HRRP), with its concise vector representation and rich structural information, has become the primary focus of research in the field of Radar Automatic Target Recognition (RATR) in Sensors community. In actual combat situations, due to limitations in sensing conditions, acquiring data from non-cooperative targets is challenging. Consequently, identifying HRRP targets in the absence of sufficient data is a significant and urgent issue that requires immediate attention. Additionally, precise target recognition represents a pressing hurdle. To this end, this paper proposes a maksed Vision Transformer (ViT) and brownian distance covariance classifier (MTBC). The ViT is initially pre-trained through a self-supervised learning strategy of masked reconstruction, which enables the model to gain a comprehensive understanding of the intricate interconnections between neighbouring blocks in HRRP, thereby facilitating the extraction of more nuanced and discriminative features. Additionally, within the context of prototype learning in few-shot metric learning, a brownian distance covariance metric-based classifier is devised. This combines a parametric feature extractor and a non-parametric metric classifier, enabling the accurate description of the dependency between support class prototypes and query vectors. Experimental results validated on measured HRRP dataset demonstrate that the proposed method performs superiorly in few-shot HRRP target recognition task.

In real-world scenarios, radar systems need to recognize both known and unknown classes, referred to as open set recognition (OSR). Prototype-like methods have shown significant potential in addressing this challenging task. However, prototype learning overlooks the directional relationship between feature embeddings and prototypes, leading to open space risk. In addition, strong learning for known classes can weaken the sensitivity to sample variations, impacting OSR performance of the model. To address these issues, we propose the regularization-based region learning (RBRL) method. Firstly, from the perspective of gradient propagation, RBRL demonstrates that l₂-regularization can enhance the sensitivity of logit vector directions to sample variations. Unlike in closed-set scenarios, l₂-regularization can effectively improve OSR performance even with sufficient training data. Secondly, RBRL minimizes the known space in both distance and angular domains by leveraging the spatial geometric relationship between prototypes and feature embeddings, mitigating open space risk. Furthermore, decision-level fusion is achieved by computing the area of the fan-shaped region in RBRL. The effectiveness of RBRL is validated on datasets comprising both measured and simulated high resolution range profiles (HRRPs). RBRL achieves state-of-the-art performance across various OSR metrics.

In recent years, HRRP-based radar automatic target recognition (RATR) using deep neural networks (DNNs) has garnered increasing attention. In real-world scenarios, test target categories frequently include both known and unknown classes, which is referred to as the open set recognition (OSR). Existing DNN-based classifiers tend to classify all captured HRRPs as known classes, presenting significant challenges for the practical application of HRRP-based RATR technology. Besides, extracting HRRP features at the optimal scale within DNNs is also essential for improving the HRRP-based OSR performance. To address these issues, we propose a systematic OSR solution named open set full-scale model (OSFSM). Firstly, OSFSM utilizes a set of convolutional kernels with prime-number lengths to efficiently extract HRRP features across all scales. Meanwhile, the learnable vectors are used to weight and filter the features at each scale. Secondly, we attribute the root cause of overconfidence in DNNs to the unconstrained magnitude of the logits. Therefore, OSFSM designs a parameter weight constraint (PWC) loss to implicitly suppress excessive growth in logits magnitude. Additionally, the prototype-based orientational (PBO) loss is proposed to reduce class cluster overlap. We validate the performance of OSFSM using two measured HRRP datasets and one simulated HRRP dataset. The OSFSM method achieved the optimal classification performance among the implemented methods.

In recent years, radar automatic target recognition (RATR) utilizing high-resolution range profiles (HRRPs) has received significant attention. Approaches based on deep learning have demonstrated remarkable efficacy in HRRP recognition tasks. However, the performance of neural networks is notably vulnerable to noise, leading to a detrimental effect on their recognition accuracy and overall robustness. To address this issue, a residual scattering attention network (RSAN) is proposed for HRRP target recognition, which comprises a residual scattering network, ResNet18, and a self-attention module. The residual scattering network is designed to suppress noise components and extract noise-robust features. It is derived from the improvement of a scattering network and does not need to learn parameters from the data. ResNet18 is employed for the purpose of extracting a deep representation of scattering features for HRRPs. Furthermore, a self-attention module is integrated into ResNet18, enabling the model to focus on target regions, thereby enhancing its feature-learning capability. The effectiveness and noise robustness of the proposed method are validated through experiments conducted on two measured datasets.