Open world recognition of communication jamming signals

Abstract

To improve the recognition ability of communication jamming signals, Siamese Neural Network-based Open World Recognition (SNN-OWR) is proposed. The algorithm can recognize known jamming classes, detect new (unknown) jamming classes, and unsupervised cluseter new classes. The network of SNN-OWR is trained supervised with paired input data consisting of two samples from a known dataset. On the one hand, the network is required to have the ability to distinguish whether two samples are from the same class. On the other hand, the latent distribution of known class is forced to approach their own unique Gaussian distribution, which is prepared for the subsequent open set testing. During the test, the unknown class detection process based on Gaussian probability density function threshold is designed, and an unsupervised clustering algorithm of the unknown jamming is realized by using the prior knowledge of known classes. The simulation results show that when the jamming-to-noise ratio is more than 0dB, the accuracy of SNN-OWR algorithm for known jamming classes recognition, unknown jamming detection and unsupervised clustering of unknown jamming is about 95%. This indicates that the SNN-OWR algorithm can make the effect of the recognition of unknown jamming be almost the same as that of known jamming.