Variable-Modulation Specific Emitter Identification With Domain Adaptation

Abstract

Specific emitter identification (SEI) is a technique of identifying individual emitters via unique characteristics of different emitters. In this paper, we consider a SEI problem with transmitter changing modulations scenario. There have been few previous studies on this type of scenario. To cope with the daunting challenge, a variable-modulation SEI framework with domain adaptation is proposed. The components characteristics of transmitter are analyzed and the distortion models are established for simulation dataset generation. The received in-phase/quadrature (I/Q) signals are demodulated and reconstructed to obtain baseband ideal modulation signals. The received signals and the ideal modulation signals corresponding to demodulation and reconstruction are merged and embedded into the feature extraction network. Domain adversarial neural network (DANN) is added into the SEI framework to generate domain-invariant fingerprint features, thus realizing variable-modulation SEI. To better align the distortion features of emitters with variable modulations, Gaussian Encoder is designed to project fingerprint features into Gaussian distribution space. Numerous experiments show that the proposed SEI framework can improve recognition accuracy of individual emitter for single modulation and variable transfer greatly, and outperform the existing transfer learning methods. The ablation study demonstrates the components of framework are complementary. The complexity of framework is acceptable and it can extend to large-scale use. The robustness of framework is verified through modulation transfer among PSK and QAM.