Specific Emitter Identification With Limited Samples: A Model-Agnostic Meta-Learning Approach

Abstract

It is necessary but difficult to obtain a large number of labeled samples to train the classification model in many real scenes. This letter proposes an approach for specific emitter identification(SEI) by introducing model-agnostic meta-learning, which can achieve high accuracy in the case of a limited number of labeled training samples. Specially, we improve the approach to make it suitable for the classification of electromagnetic signals of multiple types of equipments, without spending a lot of time and data to retrain the model structure. The data collected from ZigBee devices and UAVs are used to verify the proposed approach. The simulation results shows that the accuracy of proposed approach can reach more than 90% even though the training task and testing task are two types of devices.