Due to the complexity of real-world applications, open set recognition is often more practical than closed set recognition. Compared with closed set recognition, open set recognition needs not only to recognize known classes but also to identify unknown classes. Different from most of the current methods, we proposed three novel frameworks with kinetic pattern to address the open set recognition problems, and they are kinetic prototype framework (KPF), adversarial KPF (AKPF), and an upgraded version of the AKPF, AKPF $++$ . First, KPF introduces a novel kinetic margin constraint radius, which can improve the compactness of the known features to increase the robustness for the unknowns. Based on KPF, AKPF can generate adversarial samples and add these samples into the training phase, which can improve the performance with the adversarial motion of the margin constraint radius. Compared with AKPF, AKPF $++$ further improves the performance by adding more generated data into the training phase. Extensive experimental results on various benchmark datasets indicate that the proposed frameworks with kinetic pattern are superior to other existing approaches and achieve the state-of-the-art performance.

Radar automatic target recognition (RATR) based on high resolution range profile (HRRP) has attracted more attention in recent years. In fact, the actual application environment of RATR is open set environment rather than closed set environment. However, previous works mainly focus on closed set recognition, which classifies the known classes by dividing hyper-planes in the feature space, and it will cause classification errors in open set environment. Therefore, open set recognition is proposed to solve this problem, which needs to determine a closed classification boundary for the identification of the known and unknown targets simultaneously. To accomplish this purpose, this paper proposes and proves the extreme value boundary theorem, which demonstrates that the maximum distance from the known features to the cluster center follows the generalized extreme value distribution. According to the proposed theorem, the closed classification boundary of the cluster is easily determined to distinguish between the known and unknown classes. Finally, extensive experiments on measured HRRP data verify the validity of the proposed theorem and the effectiveness of the proposed method.