Radar HRRP Open Set Recognition Based on Extreme Value Distribution

Abstract

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.