Specific Emitter Identification Based on Deep Dense Feature Network

Abstract

In the specific emitter identification, the current traditional classification and identification methods have difficulty in extracting fingerprint features, and the accuracy of fingerprint identification is low. In order to better extract the fingerprint features, a specific emitter identification algorithm is proposed based on the Convolutional Neural Network Dense Block module, called the Deep Dense Feature Network (DDFN). The network can effectively realize automatic extraction and multiplexing of fingerprint features, strengthen the transfer of features, and reduce the problem of vanishing gradients. The experimental results show that DDFN can achieve a classification accuracy of 93.17%, and can better realize the identification of specific radiation sources in a complex electro-magnetic environment.