Specific Emitter Identification Based on Software-Defined Radio and Decision Fusion

Tan, Kaiwen; Yan, Wenjun; Zhang, Limin; Tang, Manshu; Zhang, Yan

doi:10.1109/access.2021.3088542

Cited by 9 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the output of CFNO can be expressed (33) where θ represent the trainable parameters of a specific block.…”

Section: Shortcut Connection Blockmentioning

confidence: 99%

Specific Emitter Identification Based on Paired Sample and Complex Fourier Neural Operator

Zha¹,

Li²,

Qiu³

et al. 2022

Preprint

View full text Add to dashboard Cite

<p>Specific emitter identification (SEI) techniques utilize the physical-layer fingerprints embedded in their received signals to identify the unique emitter. Fingerprints originate from hardware impairments during transmitter manufacturing. Considering the different manifestations of fingerprints in different domain perspectives, a novel neural operator with the time and frequency domain attention mechanism is proposed, which is named the complex Fourier neural operator (CFNO). And to further enhance the individual discriminability, the demodulation reconstruction waveform and the synchronized waveform are converted into a new sample format, named the paired sample. With the paired sample, the proposed network acquires prior communication knowledge and a baseline of the ideal transmitter. In this work, different network structures are investigated. And performances of the paired sample are fully evaluated. Compared with existing SEI methods based on deep learning models, results show the effectiveness of the CFNO structure, as well as the proposed paired sample scheme. The significance of this paper is that the proposed framework fully utilizes the domain knowledge in the communication field, resulting in excellent performance improvement. </p>

show abstract

“…Finally, the output of CFNO can be expressed (33) where θ represent the trainable parameters of a specific block.…”

Section: Shortcut Connection Blockmentioning

confidence: 99%

Specific Emitter Identification Based on Paired Sample and Complex Fourier Neural Operator

Zha¹,

Li²,

Qiu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The key component for individual radar emitter identification is the fingerprint feature of the radar transmitter, characterized by stability and uniqueness [4,5]. Fingerprint features persist in radar signals, resisting complete erasure due to their accidental modulation features arising from minute changes in radar technology [3,6]. Each radar must be assigned unique labels, considering that even radars of the same model may be affected by minor faults in electronics, operating time, and environmental conditions [7].…”

Section: Introductionmentioning

confidence: 99%

Specific Emitter Identification through Multi-Domain Mixed Kernel Canonical Correlation Analysis

Chen,

Li,

et al. 2024

Electronics

View full text Add to dashboard Cite

Radar specific emitter identification (SEI) involves extracting distinct fingerprints from radar signals to precisely attribute them to corresponding radar transmitters. In view of the limited characterization of fingerprint information by single-domain features, this paper proposes the utilization of multi-domain mixed kernel canonical correlation analysis for radar SEI. Initially, leveraging the complementarity across diverse feature domains, fingerprint features are extracted from four distinct domains including: envelope feature, spectrum feature, short-time Fourier transform and ambiguity function. Subsequently, kernel canonical correlation analysis is employed to amalgamate the correlation characteristics inherent in multi-domain data. Considering the insufficient of a single kernel function with only interpolation or extrapolation ability, we adopt mixed kernel to improve the projection ability of the kernel function. Experimental results substantiate that the proposed feature fusion approach maximizes the complementarity of multiple features while reducing feature dimensionality. The method achieves an accuracy of up to 95% in experiments, thereby enhancing the efficacy of radar SEI.

show abstract

“…Ref. [12] subjected the received signals to time-varying filtered empirical mode decomposition (tvf-EMD). Thereafter, the amplitude-frequency aggregation characteristics of the three-dimensional Hilbert spectrum projection and the bispectrum diagonal slice of the obtained intrinsic mode functions were used as the first and second features of SEI, respectively.…”

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

View full text Add to dashboard Cite

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.

show abstract

Specific Emitter Identification Based on Software-Defined Radio and Decision Fusion

Cited by 9 publications

References 31 publications

Specific Emitter Identification Based on Paired Sample and Complex Fourier Neural Operator

Specific Emitter Identification Based on Paired Sample and Complex Fourier Neural Operator

Specific Emitter Identification through Multi-Domain Mixed Kernel Canonical Correlation Analysis

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

Contact Info

Product

Resources

About