Open set recognition of radar specific emitter based on adversarial reciprocal point learning

Abstract

Radar specific emitter identification (SEI) is a key technology in electromagnetic spectrum control. Although the emergence of deep learning has promoted the development of SEI, there are still many shortcomings in the current research results. Most of the traditional deep learning algorithms are applicable to closed-set identification and can only be used when the database is complete. In addition, individual differences in radar signals are susceptible to noise interference, but traditional denoising methods are usually independent of the feature extraction process, making it difficult to ensure that certain individual information is not lost. Therefore, in this paper, we propose a new radar emitter open set recognition method called adversarial reciprocal point learning with adaptive denoising (ARPLAD). Firstly, we design a new feature extraction network for one-dimensional signals, which combines deep residual shrinkage network with efficient attention mechanism to autonomously denoise signals and focus on important parts of signal features. Secondly, we train the network using adversarial reciprocal point learning combined with center loss to extract discriminative features with compact intraclass distances and separable interclass distances, which can efficiently discriminate unknown signals and reduce the risk of open set identification. The experimental results show that ARPLAD exhibits excellent performance in different conditions, providing an effective solution for SEI in open electromagnetic environments.