Radar Emitter Recognition Based on the Energy Cumulant of Short Time Fourier Transform and Reinforced Deep Belief Network

Wang, Xuebao; Huang, Gan; Zhou, Zhiwen; Tian, Wei; Yao, Jialun; Gao, Jun

doi:10.3390/s18093103

Cited by 25 publications

(22 citation statements)

References 32 publications

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“…In the CD algorithm, the original state of the visible layer is set to an arbitrary training sample with the binary state of each hidden layer deduced by equation (10). After obtaining all of the states of hidden units, the probability of each visible unit can be deduced by equation (11). erefore, a reconstructed visible layer is obtained, and a reconstruction error exists between the reconstructed visible layer and the initial input visible layer.…”

Section: Rbm and Dbn First Proposed Bymentioning

confidence: 99%

“…us, extraction of unintentional modulation features from signals makes it possible to identify specific emitters. Various methods have been proposed to characterize UMOP [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. ese methods fall into categories of waveform-, transform-, and transmitterbased approaches.…”

Section: Introductionmentioning

confidence: 99%

“…ey also perform poorly when analyzing non-Gaussian and nonstationary processes. For these reasons, researchers have proposed other transforms to extract features in the transform domain, including the ambiguity function [6], the wavelet transform [7], the Hilbert-Huang transform (HHT) [8][9][10], and short-time Fourier transform [11,12]. ese transforms are appropriate for analyzing nonlinear and nonstationary signals.…”

Section: Introductionmentioning

confidence: 99%

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

Zhou

Wang

Chen

et al. 2020

Mathematical Problems in Engineering

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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.

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Section: Rbm and Dbn First Proposed Bymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhou

Wang

Chen

et al. 2020

Mathematical Problems in Engineering

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“…It is a wide range domain of R&D to solve these objectives accurately but in less complicated solutions. In the literature, algorithms exist based on neural network [6], radar signature database analysis [7], energy cumulate STFT [8], and many others.…”

Section: Pattern Analysismentioning

confidence: 99%

Power-based pulsed radar detection using wavelet denoising and spectral threshold with pattern analysis

Siblini

Audi

Ghaith

2020

Int. J. Microw. Wireless Technol.

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In this paper, an algorithm for extracting and localizing a radar pulse in a noisy environment is described. The algorithm combines two powerful tools: wavelet denoising and the short-time Fourier transform (STFT) analysis with statistical-based threshold. We aim to detect radar pulses transmitted by any radar in blind mode regardless of the intra-pulse modulation and parametric features. The use of the proposed technique makes the detection and localization of radar pulses possible under very low signal-to-noise ratio conditions (−18 dB), which leads to a reduction of the required signal power or alternatively extends the detection range of radar systems. Radar classes pattern-based analysis is used in blind mode to decrease the probability of false alarm.

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“…Kong M et al [8] used the CNN deep learning algorithm to identify the radar radiation sources, which could extract more detailed features of the radar and improve the recognition rate. To cope with the complex electromagnetic environment and varied signal styles, Wang X et al [9] proposed a novel method based on the energy cumulant of short time Fourier transform and reinforced deep belief network to gain a higher correct recognition rate for radar emitter intra-pulse signals at a low signal-to-noise ratio.…”

Section: Introductionmentioning

confidence: 99%

An Unknown Radar Emitter Identification Method Based on Semi-Supervised and Transfer Learning

et al. 2019

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Aiming at the current problem that it is difficult to deal with an unknown radar emitter in the radar emitter identification process, we propose an unknown radar emitter identification method based on semi-supervised and transfer learning. Firstly, we construct the support vector machine (SVM) model based on transfer learning, using the information of labeled samples in the source domain to train in the target domain, which can solve the problem that the training data and the testing data do not satisfy the same-distribution hypothesis. Then, we design a semi-supervised co-training algorithm using the information of unlabeled samples to enhance the training effect, which can solve the problem that insufficient labeled data results in inadequate training of the classifier. Finally, we combine the transfer learning method with the semi-supervised learning method for the unknown radar emitter identification task. Simulation experiments show that the proposed method can effectively identify an unknown radar emitter and still maintain high identification accuracy within a certain measurement error range.

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Radar Emitter Recognition Based on the Energy Cumulant of Short Time Fourier Transform and Reinforced Deep Belief Network

Cited by 25 publications

References 32 publications

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

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

Power-based pulsed radar detection using wavelet denoising and spectral threshold with pattern analysis

An Unknown Radar Emitter Identification Method Based on Semi-Supervised and Transfer Learning

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