Deep Ensemble Siamese Network For Incremental Signal Classification

Incremental Signal Classification (ISC) aims to continuously identify and classify unknown signal categories, which is essentially an open-set classification task. In this paper, a new Deep Ensemble Siamese Network (DESN) is constructed for unknown category detection and incremental accumulation of signals from the detected category. Then the accumulated samples are used to update a One-dimensional Convolution Network (OCN) for incremental learning of new signal categories. Experimental results show that the proposed method can achieve accurate detection and accumulation of unknown signals, and is feasible for practical ISC.