Specific Emitter Identification Based on Transient Energy Trajectory

Yuan, Yingjun; Huang, Zhitao; Sha, Zhi-Chao

doi:10.2528/pierc13080703

Cited by 19 publications

(11 citation statements)

References 22 publications

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“…Finally, some traditional algorithms were adopted to recognize short-wave radio station communication behavior signals. Yuan et al [ 29 ] and He et al [ 30 ] adopted respectively rectangular integral bispectrum and selected bispectra only using the magnitude matrix of the bispectral estimation, so their methods are treated as a traditional method called the “magnitude matrix of the bispectrum”. Another traditional method used in the experiment was the diagonal slice of the bispectrum [ 28 ].…”

Section: Experimental Results and Analysesmentioning

confidence: 99%

“…The slight differences are also the reason why it is difficult to distinguish communication behavior signals of a radio station from the perspective of the physical layer. At present, the frequency information or magnitude information in the bispectral estimation of signals is used separately for recognition using bispectral diagonal slices, a rectangular integral bispectrum, and a selective bispectrum [ 28 , 29 , 30 ]. In order to retain the subtle features of communication behavior signals and recognize the radio station communication behavior, this work used the improved bispectrum features as the inputs of the recognition network.…”

Section: Methodsmentioning

confidence: 99%

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Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet

Chen

Lei

2020

Sensors

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This work improves a LeNet model algorithm based on a signal’s bispectral features to recognize the communication behaviors of a non-collaborative short-wave radio station. At first, the mapping relationships between the burst waveforms and the communication behaviors of a radio station are analyzed. Then, bispectral features of simulated behavior signals are obtained as the input of the network. With regard to the recognition neural network, the structure of LeNet and the size of the convolutional kernel in LeNet are optimized. Finally, the five types of communication behavior are recognized by using the improved bispectral estimation matrix of signals and the ameliorated LeNet. The experimental results show that when the signal-to-noise ratio (SNR) values are 8, 10, or 15 dB, the recognition accuracy values of the improved algorithm reach 81.5%, 94.5%, and 99.3%, respectively. Compared with other algorithms, the training time cost and recognition accuracy of the proposed algorithm are lower and higher, respectively; thus, the proposed algorithm is of great practical value.

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Section: Experimental Results and Analysesmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet

Chen

Lei

2020

Sensors

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“…A recent work [5] confirms that RF fingerprinting is an enhancing security technique for wireless networks. The works presented in [6], [7] used RF fingerprinting for identifying individual transmitters. A study [6] proposed Specific Emitter Identification (SEI) method based on Hilbert-Huang transform (HHT).…”

Section: Introductionmentioning

confidence: 99%

“…HHT is a self-adaptive signal analysis method; therefore, no preceding information is needed in signal analysis [6]. However, a study [7] proposed SEI method based on signal's energy trajectory gained by the high order cumulants. The works in [6], [7] applied SEI to the transients of the collected GSM signals.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Features and Classifiers for Bluetooth RF Fingerprinting

2019

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Recently, network security has become a major challenge in communication networks. Most wireless networks are exposed to some penetrative attacks such as signal interception, spoofing, and stray. Radio frequency (RF) fingerprinting is considered to be a promising solution for network security problems and has been applied with various improvements. In this paper, extensive data from Bluetooth (BT) devices are utilized in RF fingerprinting implementation. Hilbert-Huang transform (HHT) has been used, for the first time, for RF fingerprinting of Bluetooth (BT) device identification. In this way, time-frequency-energy distributions (TFED) are utilized. By means of the signals' energy envelopes, the transient signals are detected with some improvements. Thirteen features are extracted from the signals' transients along with their TFEDs. The extracted features are pre-processed to evaluate their usability. The implementation of three different classifiers to the extracted features is provided for the first time in this paper. A comparative analysis based on the receiver operating characteristics (ROC) curves, the associated areas under curves (AUC), and confusion matrix are obtained to visualize the performance of the applied classifiers. In doing this, different levels of signal to noise ratio (SNR) levels are used to evaluate the robustness of the extracted features and the classifier performances. The classification performance demonstrates the feasibility of the method. The results of this paper may help readers assess the usability of RF fingerprinting for BT signals at the physical layer security of wireless networks. INDEX TERMS Bluetooth, classification, Hilbert-Huang transform, network security, radio frequency fingerprinting, wireless networks.

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“…Therefore, we need to find the exact time when the transient signal starts and ends before constructing state fingerprint. In our work, we applied the detection method proposed in [10], which required the better performance as the accurate point and tolerate calculation. Firstly, the higher order cumulants (HOC) is computed which has an important feature that the higher order cumulants (greater than second order) of Gaussian noise are permanent zero.…”

Section: Acquisition Process Of Transient Signalmentioning

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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Specific Emitter Identification Based on Transient Energy Trajectory

Cited by 19 publications

References 22 publications

Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet

Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet

Assessment of Features and Classifiers for Bluetooth RF Fingerprinting

Convolutional Neural Networks for Special State Identification Based on Transient Signal

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