Specific emitter identification based on Hilbert–Huang transform‐based time–frequency–energy distribution features

Yuan, Yingjun; Huang, Zhitao; Hao, Wu; Wang, Xiang

doi:10.1049/iet-com.2013.0865

Cited by 136 publications

(93 citation statements)

References 25 publications

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“…Generally, HHT includes EMD and Hilbert spectrum analysis [13,14]. EMD decomposes any signal into finite intrinsic mode functions (IMFs), and the decomposition bases are adaptively generated according to the original signal [16,17].…”

Section: Hht Of Radar Emittermentioning

confidence: 99%

“…High-order cumulants [7], wavelet ridge and high order spectra [8], bispectrum and its variants [9][10][11] have demonstrated the effectiveness for the given conditions. Recently, Hilbert-Huang transform (HHT) has proven the superiority in the unique representation and descriptive ability for SEI [12][13][14][15]. It provides an accurate amplitude distribution with the change of time and frequency, but does not need the prior information about the analyzed signal.…”

Section: Introductionmentioning

confidence: 99%

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Specific Emitter Identification via Feature Extraction in Hilbert-Huang Transform Domain

Zhou¹,

Zhang²,

Zhang³

2019

PIER M

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Aimed at the deficiency of conventional parameter-level methods in radar specific emitter identification (SEI), which heavily rely on empirical experience and cannot adapt to the waveform change, a novel algorithm is proposed to extract specific features and identify in Hilbert-Huang transform domain. Firstly, 2-dimensional physical representation of emitters is formed with Hilbert-Huang transform (HHT). Based on this, 4 types of multi-view features are constructed, and the feature space is spanned by elaborating the extraction. Principal components, between-class similarity, spectrum entropy, and deep architecture are used to describe the subtle features. Finally, support vector machine (SVM) is selected as the classifier to realize identification to alleviate the small sample problem. Experimental results show that the proposed algorithm realizes specific identification using 4 intentional modulations of simulated data. The selected 4 types of unintentional representations are feasible to discriminate identical emitters. Additionally, the proposed algorithm obtains higher accuracy than typical signal-level methods in the signal-to-noise ratio (SNR) range [0, 20] dB.

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Section: Hht Of Radar Emittermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Specific Emitter Identification via Feature Extraction in Hilbert-Huang Transform Domain

Zhou¹,

Zhang²,

Zhang³

2019

PIER M

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“…As we known, when an emitter is turned on, the signal goes through a transient state. As shown in paper [2], the transient signals from different emitters usually have special RF fingerprints for SEI. However, in the previous researches, the state of emitter identification is neglected in previous researches, which is necessary and useful in some legitimate applications.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Yuan. Y and Huang Z, they used the time-frequency-energy distribution based on Hibert-Huang to identify the special emitter [2]. Also, the empirical mode decomposition(EMD) was used to obtain the instantaneous amplitude and frequency information as RF fingerprint [4] .…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks for Special State Identification Based on Transient Signal

Ding¹,

Li²,

Wu³

2017

dtetr

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Abstract. Most exiting algorithm focused on the specific emitter identification, neglected the state identification of emitter. However, using the fingerprinting to identify and estimate the user behavior is useful in many legitimate scenes.While the emitter is moving or shaking, a certain amount of vibrations are generated, which can be investigated as the basis of state identification. However, the subtle vibrations are difficult to capture. In this paper, we propose a novel state analysis technique to identify the state of emitter based on transient signal. The transient signal from the target emitters contains import special information. In order to find the subtle difference, 1-D convolution neural networks (CNNs) is introduced to extract the latent features adaptively. Experimental results have shown that our method can work for specific state identification.

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“…Many features were extracted from TFED of signal, which can represent the characteristics of signal in both time and frequency domains. Thirteen features of four types were extracted from TFED of transient signals to classify different kind of mobile phones [5]. Shuhua Xu et al extracted features using integral bispectrum of signals [6,7].…”

Section: Introductionmentioning

confidence: 99%

Specific Emitter Identification based on the Energy Envelope of Transient Signal

Zhuo¹,

Huang²,

Chen³

2016

Proceedings of the 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer

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Abstract. Specific Emitter Identification (SEI) is the method to identify the individual radio emitter using the transmitted signals' characteristic called Radio Frequency Fingerprint (RFF), which are originated from transmitter imperfections. A novel SEI approach to extract transient fingerprint features of energy envelope of transient signals is proposed in this paper. The origin of energy envelop is explored, and a nonlinear system model is utilized to explain how the features of energy envelope produce. An iterative least squares identification algorithm is proposed to extract the parameters of model, which can be constructed the fingerprint features. Experimental results demonstrate that the method is effective, and the method can be applicable any signal of TDMA system.

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Specific emitter identification based on Hilbert–Huang transform‐based time–frequency–energy distribution features

Cited by 136 publications

References 25 publications

Specific Emitter Identification via Feature Extraction in Hilbert-Huang Transform Domain

Specific Emitter Identification via Feature Extraction in Hilbert-Huang Transform Domain

Convolutional Neural Networks for Special State Identification Based on Transient Signal

Specific Emitter Identification based on the Energy Envelope of Transient Signal

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