Deep metric learning for robust radar signal recognition
Kuiyu Chen, Jingyi Zhang, Si Chen, Shuning Zhang
Abstract
Signal recognition technology is a currently active area in both civilian and military applications. Recently, deep learning has aroused extensive attempts in radar signal recognition due to its remarkable capability of automatic feature extraction. However, existing radar signal recognition networks overly depend on the probability-based decision model, resulting in poor robustness. This paper develops a novel deep metric learning frame to enhance the robustness of the recognition system. First, a multiscale atrous pyramid network (MAPNet) is proposed to efficiently learn high-resolution and distinct feature representation. Second, a variance loss is designed to constrain the intra-class feature distribution in metric space. Third, according to the distribution of training signals in metric space, recognition results are recalibrated to provide explicit rejection probabilities for unknowns. Extensive experiments and evaluations demonstrate that the proposed model can accurately classify known signals while robustly identifying unknown signals. The signal database and model can be freely accessed at https://github.com/bryantky/MAPNet .