LAUSR

In this letter, we study the problem of jamming pattern recognition (JPR) when some patterns have no training samples. Existing works were designed for recognition of known jamming patterns only, hence they failed to deal with unknown jamming patterns. To address this challenge, a zero-shot learning (ZSL) based JPR scheme is proposed. Firstly, a supervised training process is introduced to learn the latent feature representation of known jamming patterns. Then, an unsupervised classification approach is proposed to recognize different jamming patterns. Finally, both known and unknown jamming patterns are classified in latent feature space. Simulation results show that the proposed scheme achieves excellent performance when dealing with the open-set JPR tasks.