An Adaptive Specific Emitter Identification System for Dynamic Noise Domain

Zeng, Miyi; Liu, Zhigang; Wang, Zhengwei; Liu, Hong; Li, Yuzhen; Yang, Hongyu

doi:10.1109/jiot.2022.3195450

Cited by 19 publications

(12 citation statements)

References 49 publications

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“…This work is mainly inspired by our previous work [13], which proposed a robust RFF extractor denoted as improving SWT by energy regularization (ISWTE), enhancing the performance of SEI at low SNRs. In a previous work [13], we fully discussed the advantages of wavelet transformation in improving RFF features and conducted experiments to compare ISWTE, SWT, spectrograms, and traditional signal processing methods. We concluded that SWT outperforms other RFF extractors in terms of accuracy and efficiency.…”

Section: Rff Feature Extracting and Unifying Mswteumentioning

confidence: 99%

“…For example, in fast flight mode, high-frequency signals are extremely sensitive to Doppler shifts, while low-frequency signals are less affected by speed changes. Moreover, aircraft broadcast multi-modal signals, such as automatic dependent surveillance-broadcast (ads-b) signals, air traffic control (atc) signals [13], and radar signals, enabling accurate detection and guidance by air traffic controllers on the ground. Reference [14] explored the RFFs of aircraft based on radar signals.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the SWT energy explosion issue remains unresolved. Reference [13] proposed that energy regularization for combining SWT layers can enhance the RFF features. Furthermore, an energy-balancing strategy can unify the semantics of different modal signals and improve RFF, as energy accumulation and computation have always been important for signal processing and analysis in emitter identification.…”

mentioning

confidence: 99%

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A Specific Emitter Identification System Design for Crossing Signal Modes in the Air Traffic Control Radar Beacon System and Wireless Devices

Zeng,

Yao,

Liu

et al. 2023

Sensors

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To improve communication stability, more wireless devices transmit multi-modal signals while operating. The term ‘modal’ refers to signal waveforms or signal types. This poses challenges to traditional specific emitter identification (SEI) systems, e.g., unknown modal signals require extra open-set mode identification; different modes require different radio frequency fingerprint (RFF) extractors and SEI classifiers; and it is hard to collect and label all signals. To address these issues, we propose an enhanced SEI system consisting of a universal RFF extractor, denoted as multiple synchrosqueezed wavelet transformation of energy unified (MSWTEu), and a new generative adversarial network for feature transferring (FTGAN). MSWTEu extracts uniform RFF features for different modal signals, FTGAN transfers different modal features to a recognized distribution in an unsupervised manner, and a novel training strategy is proposed to achieve emitter identification across multi-modal signals using a single clustering method. To evaluate the system, we built a hybrid dataset, which consists of multi-modal signals transmitted by various emitters, and built a complete civil air traffic control radar beacon system (ATCRBS) dataset for airplanes. The experiments show that our enhanced SEI system can resolve the SEI problems associated with crossing signal modes. It directly achieves 86% accuracy in cross-modal emitter identification using an unsupervised classifier, and simultaneously obtains 99% accuracy in open-set recognition of signal mode.

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Section: Rff Feature Extracting and Unifying Mswteumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A Specific Emitter Identification System Design for Crossing Signal Modes in the Air Traffic Control Radar Beacon System and Wireless Devices

Zeng,

Yao,

Liu

et al. 2023

Sensors

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“…Researchers have shown interest in combining GAN networks with SEI in recent years. Zeng et al [17] introduced an approach that utilizes an unsupervised neural network GAN (NEGAN) to extract noise features from radiation sources. This method reduces the dependency of classification models on data set quality.…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Networks with Gramian Angular Field for Handling Imbalanced Data in Specific Emitter Identification

Zhang,

Zhou,

2023

Preprint

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Specific Emitter Identification (SEI) is a methodology employed to identify emitters by exploiting the hardware impairments inherent in transmitting devices. In the real world, there are challenges in performing SEI on radiation source signals, such as serious imbalance among samples, leading to low model accuracy, poor generalization, and limited practical application. These challenges widely occur in modern military, security, and other fields, but related research works have been conducted relatively late. In this paper, we propose a Generative Adversarial Network (GAN) with the Gramian Angular Field (GAF) method to address few-shot case data. Specifically, the proposed method employs GAF transformation to convert temporal radar data into a two-dimensional image format and utilizes an enhanced GAN to improve the classifier for imbalanced data based on the characteristics of GAF through training on both augmented and original samples. The experiments were conducted on real-world Automatic Dependent Surveillance-Broadcast (ADS-B) signals, demonstrating the effectiveness of the proposed method. The method could significantly improve the performance of the SEI model in inter-class imbalanced scenarios.

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“…Only limited research has been performed in the past on solving the long time span SEI problem: Ref. [34] developed an adaptive SEI system for the dynamic noise domain. The authors proposed a preprocessing algorithm called improving synchrosqueezed wavelet transforms by energy regularization, and an unsupervised neural network noise feature extracting GAN (NEGAN) (note that "GAN" means generative adversarial network).…”

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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An Adaptive Specific Emitter Identification System for Dynamic Noise Domain

Cited by 19 publications

References 49 publications

A Specific Emitter Identification System Design for Crossing Signal Modes in the Air Traffic Control Radar Beacon System and Wireless Devices

A Specific Emitter Identification System Design for Crossing Signal Modes in the Air Traffic Control Radar Beacon System and Wireless Devices

Generative Adversarial Networks with Gramian Angular Field for Handling Imbalanced Data in Specific Emitter Identification

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

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