Individual Identification of Radar Emitters Based on a One-Dimensional LeNet Neural Network

Abstract

Specific emitter identification involves extracting the fingerprint features that represent the individual differences of the emitter through processing the received signals. By identifying the extracted fingerprint features, one can also identify the emitter to which the received signals belong. Due to differences in transmitter hardware, this fingerprint cannot be duplicated. Therefore, SEI plays an important role in the field of information security and can reduce the information leakages caused by key theft. This method can also be used in the military field to support communication countermeasures via emitter individual identification. In this paper, empirical mode decomposition is carried out for each radar pulse signal, and then the bispectral features are extracted. Dimensionality reduction is carried out according to the symmetry of the bispectral features. The features after dimensionality reduction are input into a one-dimensional LeNet neural network as the fingerprint features of the emitter, and the identification of 10 radar emitter sources is completed. Based on the verification of real signals, the SEI identification strategy in this paper achieved a recognition rate of 96.4% for 10 radar signals, 98.9% for 10 data emitter signals, and 88.93% for 5 communication radio signals.

Dual-Branch feature fusion residual neural network for individual radar signal recognition in low SNR environments

“…These local detections are then aggregated to form a complete picture. In radar signal analysis, this translates to 1D-CNNs for processing 1D Intermediate Frequency (IF) signals [8] and 2D-CNNs for analyzing 2D time-frequency images, optimizing the network structure for different data dimensions [9].…”

Structural inversion of radar emitter based on stacked convolutional autoencoder and deep neural network

“…Radar emitter recognition (RER) [2] is one of the main functions of radar countermeasure systems and includes modulation type recognition, waveform recognition [3][4][5][6], and specific emitter identification (SEI) [7]. Since the deep learning method was introduced to SEI [8], the methods for the fine feature extraction of radar signals have become increasingly more abundant, and the use of feature extraction via deep learning methods is on the rise [9,10]. At present, feature extraction can be carried out from the three aspects of the time domain, frequency domain, and time-frequency domain.…”

“…The proposed method has a high recognition accuracy and can adapt to a variety of different radiation sources. Time-frequency analysis methods also have important applications in feature extraction, including the short-time Fourier transform (STFT), Wigner-Ville distribution (WVD), S-transform (ST), continuous wavelet transform (CWT), Hilbert-Huang transform (HHT), wavelet transform (WT), and the Choi-Williams distribution (CWD) [17][18][19][20][21][22][23][24]. As information is calculated from only one domain, important features with a high resolution are discarded.…”

A Radar Emitter Recognition Mechanism Based on IFS-Tri-Training Classification Processing

“…Many researchers improved those algorithms in response to the aforementioned issues. One study empirically decomposed each radar pulse signal, extracted bispectral features, and fed the reduced bispectrum features into the one-dimensional LeNet neural network as the transmitter's fingerprint features [21]. Some other studies employed different techniques, such as the support vector machine (SVM) radar emitter identification method, which circumvents the slow processing speed of SVM on large sample data based on affinity propagation (AP) clustering [22]; or the hierarchical extreme learning machine (BS+H-ELM), in which the sparse autoencoder (AE) in the H-ELM is used for feature learning after the bispectrum of the radar signal is extracted [23].…”