Siamese Network-based Open Set Identification of Communications Emitters with Comprehensive Features

Wu, Yufeng; Sun, Zhuo; Yue, Gang

doi:10.1109/ccisp52774.2021.9639257

Cited by 12 publications

(4 citation statements)

References 13 publications

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“…SiameseNet SiameseNet can effectively distinguish the sample from one categories from other categories. Y. Wu et al [44] and G. Sun [45] introduced it into SEI for the tasks of open-set SEI and FS-SEI, respectively. In addition, Z. Zhang et al [46] and P. Man et al [47] applied the relation network, which is an improved SiameseNet and uses a fully connected neural network to judge the degree of similarity, for signal modulation recognition and zero-shot SEI.…”

Section: Typical Fsl Methods and Their Applicationsmentioning

confidence: 99%

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Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning

Wang

Gui

Lin

et al. 2022

IEEE Internet Things J.

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Specific emitter identification (SEI) is a highly potential technology for physical layer authentication that is one of the most critical supplement for the upper-layer authentication. SEI is based on radio frequency (RF) features from circuit difference, rather than cryptography. These features are inherent characteristic of hardware circuits, which difficult to counterfeit. Recently, various deep learning (DL)based conventional SEI methods have been proposed, and achieved advanced performances. However, these methods are proposed for close-set scenarios with massive RF signal samples for training, and they generally have poor performance under the condition of limited training samples. Thus, we focus on few-shot SEI (FS-SEI) for aircraft identification via automatic dependent surveillance-broadcast (ADS-B) signals, and a novel FS-SEI method is proposed, based on deep metric ensemble learning (DMEL). Specifically, the proposed method consists of feature embedding and classification. The former is based on metric learning with complex-valued convolutional neural network (CVCNN) for extracting discriminative features with compact intra-category distance and separable inter-category distance, while Y. Wang and G. Gui are with the

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Section: Typical Fsl Methods and Their Applicationsmentioning

confidence: 99%

“…3) Two contrastive loss-based methods: Here, we adopt two contrastive loss-based signal recognition methods for comparison. The one of the contrastive losses is originate from Siame-seNet [44]- [47], which has been introduced in Part II. The other is triplet loss, which has been specifically introduced above.…”

Section: ) Softmax Cvcnn and Direct Cvcnnmentioning

confidence: 99%

Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning

Wang

Gui

Lin

et al. 2022

IEEE Internet Things J.

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“…There is relatively little research on OSR algorithms in the field of signals. Wu et al [26] proposed an OSR method based on the Siamese network for feature extraction and recognition of mobile Bluetooth signals. In Ref.…”

Section: Related Workmentioning

confidence: 99%

Research on the Enhancement Method of Specific Emitter Open Set Recognition

Sun,

Du,

Qiao

et al. 2023

Electronics

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Open set recognition (OSR) aims at dealing with unknown classes that are not included in the train set. However, existing OSR methods rely on deep learning networks that perform supervised learning on known classes in the train set, resulting in poor performance when the unknown class is very similar to the known class. Considering the subtle individual differences under the same type in specific emitter identification (SEI) applications, it is difficult to distinguish between known classes and unknown classes in open set scenarios. This paper proposes a pseudo signal generation and recognition neural network (PSGRNN) to address relevant problems in this situation. PSGRNN applies complex-value convolution operations to accommodate IQ signal inputs. Its key idea is to utilize samples of known classes to generate pseudo samples of unknown classes. Then, the samples of known classes and the generated pseudo samples of unknown classes are jointly input into the neural network to construct a new classification task for training. Moreover, the center loss is improved by adding inter-class penalties to maximize the inter-class difference. This helps to learn useful information for separating known and unknown classes, resulting in clearer decision boundaries between the known and the unknown. Extensive experiments on various benchmark signal datasets indicate that the proposed method achieves more accurate and robust open set classification results, with an average accuracy improvement of 4.62%.

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“…Hassen et al [32] proposed learning a neural network-based representation for OSR that incentivizes projecting class samples around the class mean directly. Using Siamese networks, Wu et al [33] proposed an identification method for open set emitters that relies on classification loss, reconstruction loss, and clustering loss. Yue et al [34] improved the learned spectral-spatial features by reconstructing the spectral and spatial features.…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution Feature Extraction Network for Open Set Recognition of Electromagnetic Signal

Zhang,

Zhou,

Wang

et al. 2024

CMES

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This paper proposes a novel open set recognition method, the Spatial Distribution Feature Extraction Network (SDFEN), to address the problem of electromagnetic signal recognition in an open environment. The spatial distribution feature extraction layer in SDFEN replaces convolutional output neural networks with the spatial distribution features that focus more on inter-sample information by incorporating class center vectors. The designed hybrid loss function considers both intra-class distance and inter-class distance, thereby enhancing the similarity among samples of the same class and increasing the dissimilarity between samples of different classes during training. Consequently, this method allows unknown classes to occupy a larger space in the feature space. This reduces the possibility of overlap with known class samples and makes the boundaries between known and unknown samples more distinct. Additionally, the feature comparator threshold can be used to reject unknown samples. For signal open set recognition, seven methods, including the proposed method, are applied to two kinds of electromagnetic signal data: modulation signal and real-world emitter. The experimental results demonstrate that the proposed method outperforms the other six methods overall in a simulated open environment. Specifically, compared to the state-of-the-art Openmax method, the novel method achieves up to 8.87% and 5.25% higher micro-F-measures, respectively.

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Siamese Network-based Open Set Identification of Communications Emitters with Comprehensive Features

Cited by 12 publications

References 13 publications

Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning

Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning

Research on the Enhancement Method of Specific Emitter Open Set Recognition

Spatial Distribution Feature Extraction Network for Open Set Recognition of Electromagnetic Signal

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