Dynamic Boundary Adversarial Model for Open-Set Radar-Specific Emitter Identification
Zehuan Jing, Peng Li, Xu Zhou, Erxing Yan, Yingchao Chen, Jingyi Li, Zhao Wang · IEEE Transactions on Aerospace and Electronic Systems · 2025
In the real world, radar specific emitter identification (SEI) faces complex open set recognition (OSR) scenarios, where traditional closed-set classifiers cannot distinguish between known and unknown categories similarly distributed in the feature space. Furthermore, feature overlap in the open space challenges the design of judgment thresholds. Different from most current approaches, we propose a dynamic boundary adversarial model based on convolutional prototype learning (CPL-DBA). It assigns magnetization weights of various directions and magnitudes to samples during training, depending on the features' distances from the prototype center. As a result, the model clusters samples that are closer to the prototype center while repelling extreme samples farther away. Additionally, we enhance the detection performance of unknown classes by introducing dynamic compact constraint terms. We design trainable closed dynamic boundaries for each known class prototype. This allows for adaptive expansion and contraction of the closed classification boundaries. Consequently, the dynamic boundaries improve the compactness of known features, which can also serve as detection thresholds for unknown classes. Extensive experimental results on the three simulated datasets, one simulator acquisition dataset, and one actual collected dataset indicate that the proposed dynamic framework achieves the best performance compared to other OSR methods.