Specific Emitter Identification Using Adaptive Signal Feature Embedded Knowledge Graph

Hua, Minyu; Zhang, Yibin; Sun, Jinlong; Adebisi, Bamidele; Ohtsuki, Tomoaki; Gui, Guan; Wu, Hsiao-Chun; Sari, Hikmet

doi:10.1109/jiot.2023.3300689

“…At present, object detection is being well applied in the field of computer vision [3][4][5], and it has become possible to detect bridge diseases in different environments. It worth noting that object detection is different from the deep learning based automatic modulation classification [6,7], specific emitter identification [8][9][10] and malware traffic classification [11,12]. Object detection algorithms are divided into two categories: two-stage detection [13][14][15][16] and single detection [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Small target disease detection based on YOLOv5 framework for intelligent bridges

Zhang,

Xiong,

Jiang

et al. 2024

Peer-to-Peer Netw. Appl.

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This paper proposes a small target disease detection method using YOLOv5 framework for detecting small apparent diseases on intelligent bridges, aiming to address the problem of missed and false detection. To enhance the detection of small apparent diseases, a layer for detecting small objects is added to the YOLOv5 model. Additionally, an ECA attention mechanism module is embedded in the feature enhancement network to improve the extraction of disease features. To validate the effectiveness of the proposed algorithm, a dataset of 996 bridges with apparent diseases such as corrosion, rebar, speckle, hole and spall was established and trained after manual annotation and data augmentation. The experiment showed that the proposed algorithm achieves a mAP of 87.91%. Compared to the original YOLOv5 model, the proposed algorithm improved the mAP on the bridge apparent disease dataset by 1.97%. This algorithm accurately detects small apparent diseases on bridges and effectively reduces missed detection.

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“…The electromagnetic environment of the battlefield has become increasingly complex due to the continuous development and widespread use of new radar systems [1]. As a crucial aspect of electronic reconnaissance, radar individual identification has garnered significant attention from scholars [1][2][3][4][5][6]. This technology relies on the distinctive individual features of radar signals to accurately identify different radar transmitters in complex environments [7].…”

Section: Introductionmentioning

confidence: 99%

“…Based on this, Wang Lei [21] used fuzzy functions to analyze the phase noise and various unique parasitic signals generated during transmitter emission and use it as a feature to aid recognition. M. Hua [6] proposed an improved deep learning-based SEI method using a signal feature embedded knowledge graph composed of universal features. Kawalec [22] et al, systematically analyzed the time domain waveforms of radar signals and obtained the fine fingerprint features of pulse envelopes by demodulating them, including pulse width, envelope rising edge, trailing edge, envelope top drop, etc.…”

Section: Introductionmentioning

confidence: 99%

Attention-Enhanced Dual-Branch Residual Network with Adaptive L-Softmax Loss for Specific Emitter Identification under Low-Signal-to-Noise Ratio Conditions

Jing,

Li,

Wu

et al. 2024

Remote Sensing

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To address the issue associated with poor accuracy rates for specific emitter identification (SEI) under low signal-to-noise ratio (SNR) conditions, where the single-dimension radar signal characteristics are severely affected by noise, we propose an attention-enhanced dual-branch residual network structure based on the adaptive large-margin Softmax (ALS). Initially, we designed a dual-branch network structure to extract features from one-dimensional intermediate frequency data and two-dimensional time–frequency images, respectively. By assigning different attention weights according to their importance, these features are fused into an enhanced joint feature for further training. This approach enables the model to extract distinctive features across multiple dimensions and achieve good recognition performance even when the signal is affected by noise. In addition, we have introduced L-Softmax to replace the original Softmax and propose the ALS. This approach adaptively calculates the classification margin decision parameter based on the angle between samples and the classification boundary and adjusts the margin values of the sample classification boundaries; it reduces the intra-class distance for the same class while increasing the inter-class distance between different classes without the need for cumbersome experiments to determine the optimal value of decision parameters. Our experimental findings revealed that, in comparison to alternative methods, our proposed approach markedly enhances the model’s capability to extract features from signals and classify them in low-SNR environments, thereby effectively diminishing the influence of noise. Notably, it achieves the highest recognition rate across a range of low-SNR conditions, registering an average increase in recognition rate of 4.8%.

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“…At present, object detection is being well applied in the field of computer vision [3][4][5], and it has become possible to detect bridge diseases in different environments. It worth noting that object detection is different from the deep learning based automatic modulation classification [6,7], specific emitter identification [8][9][10] and malware traffic classification [11,12]. Object detection algorithms are divided into two categories: two-stage detection [13][14][15][16] and single detection [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Small Target Disease Detection based on YOLOv5 Framework for Intelligent Bridges

Zhang,

Xiong,

Jiang

et al. 2023

Preprint

Self Cite

0

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This paper proposes a small target disease detection method using YOLOv5 framework for detecting small apparent diseases on intelligent bridges, aiming to address the problem of missed and false detection. To enhance the detection of small apparent diseases, a layer for detecting small objects is added to the YOLOv5 model. Additionally, an ECA attention mechanism module is embedded in the feature enhancement network to improve the extraction of disease features. To validate the effectiveness of the proposed algorithm, a dataset of 996 bridges with apparent diseases such as corrosion, rebar, speckle, hole and spall was established and trained after manual annotation and data augmentation. The experiment showed that the proposed algorithm achieves a mAP of 87.91%. Compared to the original YOLOv5 model, the proposed algorithm improved the mAP on the bridge apparent disease dataset by 1.97%. This algorithm accurately detects small apparent diseases on bridges and effectively reduces missed detection.

show abstract

Specific Emitter Identification Using Adaptive Signal Feature Embedded Knowledge Graph

Cited by 13 publications

References 47 publications

Small target disease detection based on YOLOv5 framework for intelligent bridges

Small target disease detection based on YOLOv5 framework for intelligent bridges

Attention-Enhanced Dual-Branch Residual Network with Adaptive L-Softmax Loss for Specific Emitter Identification under Low-Signal-to-Noise Ratio Conditions

Small Target Disease Detection based on YOLOv5 Framework for Intelligent Bridges

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