A probe-feature for specific emitter identification using axiom-based grad-CAM

Abstract

Specific emitter identification (SEI) is the process of discriminating different emitters based on the radio fingerprints extracted from the received signals. Without complex parameter adjustment, radio fingerprints can be efficiently extracted by end-to-end deep learning models. However, deep learning models usually rely on a large number of training samples, which can not be provided in the SEI tasks due to data confidentiality. Besides, the mechanism of deep learning models still lacks clear interpretation, reducing the user’s trust in the extracted radio fingerprints for the SEI. In this paper, we propose an interpretable SEI feature, based on the convolutional neural network (CNN), Probe-Feature, resorting to axiom-based Class Activation Mapping (XGrad-CAM). In general, the XGrad-CAM is firstly used as a probe into the CNN to discern important regions of the original features contributive to the current classification. Subsequently, those regions are fused into a discriminative feature with vivid extra-class differences. Experimental results demonstrate that the Probe-Feature can significantly improve the CNN’s classification accuracy compared to the original features as inputs, especially when only scanty training samples are provided. In addition, Probe-Feature also sheds some light on interpreting CNN’s classification with the SEI signals by two axioms, sensitivity, and conservation, used in XGrad-CAM.