Specific Emitter Identification via Sparse Bayesian Learning Versus Model-Agnostic Meta-Learning

Abstract

Specific emitter identification (SEI) is a technique to identify the unknown emitters by using the hardware impairment of the transmitter. In this paper, we consider the effect of the wireless channel on the SEI, which deteriorates the identification performance severely. Two identifiers are proposed to address the wireless channel effect from the model-based and the data-based perspectives, respectively. From the model-based perspective, the fingerprint extractor using the sparse Bayesian learning (SBL) is first proposed to jointly estimate the fingerprint parameters, the wireless channel, and the noise power in the multipath fading channels. Then, the classifier using the weighted Euclidean distance is designed to identify the unknown emitter. From the data-based perspective, the model-agnostic meta-learning (MAML) algorithm is adopted to meta-train the convolutional neural network (CNN) on the task collection, which is generated based on the transmitter distortion mechanism and the channel distribution. The trained CNN is fine-tuned on the unseen SEI task and then is used to identify the unknown emitter. Moreover, the Cramer-Rao lower bounds of the estimation of the fingerprint parameters are derived to evaluate the performance of the proposed fingerprint extractor. Numerical results show that the SBL identifier outperforms the MAML one in a small number of samples, while the MAML identifier outperforms the SBL one in a large number of samples. Both identifiers are robust to the wireless channel, obtain better identification performance, and require a small number of samples compared to the existing methods. Furthermore, the simulation results indicate that the mean squared error performance of the SBL fingerprint extractor is close to the performance lower bound.