On the Identification Accuracy of the I/Q Imbalance-Based Specific Emitter Identification

Niu, Yun; Wang, Xiong; Li, Z. H.; Dong, Binhong; Fu, Xiaoyu

doi:10.1109/access.2023.3296688

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Refs. [6,7] used I/Q amplitude and phase imbalance as an example to study the accurate identification problem of different transmitters. Ref.…”

Section: Introductionmentioning

confidence: 99%

A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation

Liu,

Guo,

Zhao

et al. 2023

Remote Sensing

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Specific emitter identification (SEI) is a professional technology to recognize different emitters by measuring the unique features of received signals. It has been widely used in both civilian and military fields. Recently, many SEI methods based on deep learning have been proposed, most of which assume that the training set and testing set have the same data distribution. However, in reality, the testing set is generally used later than the training set and lacks labels. The long time span may change the signal transmission environment and fingerprint features. These changes result in considerable differences in data distribution between the training and testing sets, thereby affecting the recognition and prediction abilities of the model. Therefore, the existing works cannot achieve satisfactory results for a long time span SEI. To address this challenge and obtain stable fingerprints, we transform the long time span SEI problem into a domain adaptive problem and propose an unsupervised domain adaptive method called LTS-SEI. Noteworthily, LTS-SEI uses a multilayer convolutional feature extractor to learn feature knowledge and confronts a domain discriminator to generate domain-invariant shallow fingerprints. The classifier of LTS-SEI applies feature matching to source domain samples and target domain samples to achieve the domain alignment of deep fingerprints. The classifier further reduces the intraclass diversity of deep features to alleviate the misclassification problem of edge samples in the target domain. To confirm the effectiveness and reliability of LTS-SEI, we collect multiple sets of real satellite navigation signals using two antennas with 13 m- and 40 m-large apertures, respectively, and construct two available datasets. Numerous experiments demonstrate that LTS–SEI can considerably increase the recognition accuracy of the long time span SEI and is superior to the other existing methods.

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“…Refs. [6,7] used I/Q amplitude and phase imbalance as an example to study the accurate identification problem of different transmitters. Ref.…”

Section: Introductionmentioning

confidence: 99%

A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation

Liu,

Guo,

Zhao

et al. 2023

Remote Sensing

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“…The results demonstrated acquired 88% identification accuracy and a 12.8% false alarm rate. The study [16] studied the IQ Imbalance parameter using SVM classification with MATLAB to simulate 5 analog modulators. The accuracy was ≥90% for SNR ≥ 15 dB.…”

Section: Introductionmentioning

confidence: 99%

Threat Detection Model for WLAN of Simulated Data Using Deep Convolutional Neural Network

Bashi,

Jameel,

Kubaisi

et al. 2023

Applied Sciences

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Security identification solutions against WLAN network attacks according to straightforward digital detectors, such as SSID, IP addresses, and MAC addresses, are not efficient in identifying such hacking or router impersonation. These detectors can be simply mocked. Therefore, a further protected key uses new information by combining these simple digital identifiers with an RF signature of the radio link. In this work, a design of a convolutional neural network (CNN) based on fingerprinting radio frequency (RF) is developed with computer-generated data. The developed CNN is trained with beacon frames of a wireless local area network (WLAN) that is simulated as a result of identified and unidentified router nodes of fingerprinting RF. The proposed CNN is able to detect router impersonators by comparing the data pair of the MAC address and RF signature of the received signal from the known and unknown routers. ADAM optimizer, which is the extended version of stochastic gradient descent, has been used with a developed deep learning convolutional neural network containing three fully connected and two convolutional layers. According to the training progress graphic, the network converges to around 100% accuracy within the first epoch, which indicates that the developed architecture was efficient in detecting router impersonations.

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Specific Emitter Identification Based on IQ Imbalance and PA Nonlinearity Joint Estimation

Liu,

2023

2023 9th International Conference on Computer and Communications (ICCC)

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On the Identification Accuracy of the I/Q Imbalance-Based Specific Emitter Identification

Cited by 3 publications

References 19 publications

A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation

A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation

Threat Detection Model for WLAN of Simulated Data Using Deep Convolutional Neural Network

Specific Emitter Identification Based on IQ Imbalance and PA Nonlinearity Joint Estimation

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