Specific Emitter Identification via Bispectrum-Radon Transform and Hybrid Deep Model

Yipeng Zhou,You Chen,Yuanrong Tian,Xing Wang

doi:10.1155/2020/7646527

Abstract

Specific emitter identification is a technique that distinguishes different emitters using radio fingerprints. Feature extraction and classifier selection are critical factors affecting SEI performance. In this paper, we propose an SEI method using the Bispectrum-Radon transform (BRT) and a hybrid deep model. We propose BRT to characterize the unintentional modulation of pulses due to the superiority of bispectrum distributions in characterizing nonlinear features of signals. We then apply a hybrid deep model based on denoising autoencoders and a deep belief network to perform further deep feature extraction and discriminative identification. We design an automatic dependent surveillance-broadcast signal acquisition system to capture signals and to build dataset for validating our proposed SEI method. Theoretical analysis and experimental results show that the BRT feature outperformed traditional features in characterizing UMOP, and our proposed SEI method outperformed other feature and classifier combination methods.

Highlights

Specific emitter identification (SEI) is a technique that distinguishes different emitters using radio frequency fingerprints. e technique has attracted much attention in recent decades, meeting many application requirements in electronic intelligence, cognitive radio, wireless network security, and Internet of things
(ii) We propose an SEI method. e method extracts the unintentional modulation on pulses (UMOPs) features via Bispectrum-Radon transform (BRT) and uses a hybrid deep model to perform deep feature extraction and discriminative identification
E denoising autoencoders (DAE) model used in our experiment has two stacked autoencoders and a backpropagation layer. e DAE model has an architecture of (185, 100, 60). e DBN model consisted of two stacked restricted Boltzmann machine (RBM) with an architecture of (185, 120, 60). e LeNet-5 used in this paper consists of two sets of conventional and average pooling layers, followed by two fully connected layers and a softmax classifier. e size of the input image in LeNet-5 is 28 × 28, so we extended the one-dimensional feature to 1 × 784 using cubic spline interpolation and further reshaped to 28 × 28

Summary

Introduction

Specific emitter identification (SEI) is a technique that distinguishes different emitters using radio frequency fingerprints. e technique has attracted much attention in recent decades, meeting many application requirements in electronic intelligence, cognitive radio, wireless network security, and Internet of things. Compared with feature extraction methods in the time and frequency domains, the higherorder spectrum has three characteristics: (a) it contains amplitude and phase information simultaneously, (b) it includes the nonlinear component of signals, and (c) the higher-order cumulant of Gaussian noise equals to zero. Owing to these characteristics, it is possible to extract UMOP features using the higher-order spectrum. In the proposed SEI method, the BRT extracts UMOP features while the hybrid deep learning model performs further feature learning and identification. Owing to the characteristics mentioned above, UMOP is extremely useful in solving SEI problems, making the choice of features for characterizing UMOP a significant research focus for SEI

Bispectrum-Radon Transform

Hybrid Deep Model

Findings

Experiments and Discussion

Conclusions