Spatial Distribution Feature Extraction Network for Open Set Recognition of Electromagnetic Signal

Abstract

This paper proposes a novel open set recognition method, the Spatial Distribution Feature Extraction Network (SDFEN), to address the problem of electromagnetic signal recognition in an open environment. The spatial distribution feature extraction layer in SDFEN replaces convolutional output neural networks with the spatial distribution features that focus more on inter-sample information by incorporating class center vectors. The designed hybrid loss function considers both intra-class distance and inter-class distance, thereby enhancing the similarity among samples of the same class and increasing the dissimilarity between samples of different classes during training. Consequently, this method allows unknown classes to occupy a larger space in the feature space. This reduces the possibility of overlap with known class samples and makes the boundaries between known and unknown samples more distinct. Additionally, the feature comparator threshold can be used to reject unknown samples. For signal open set recognition, seven methods, including the proposed method, are applied to two kinds of electromagnetic signal data: modulation signal and real-world emitter. The experimental results demonstrate that the proposed method outperforms the other six methods overall in a simulated open environment. Specifically, compared to the state-of-the-art Openmax method, the novel method achieves up to 8.87% and 5.25% higher micro-F-measures, respectively.