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Keywords = emitter recognition

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22 pages, 3666 KB  
Article
Resource-Constrained Specific Emitter Identification Based on Efficient Design and Network Compression
by Mengtao Wang, Shengliang Fang, Youchen Fan and Shunhu Hou
Sensors 2025, 25(7), 2293; https://doi.org/10.3390/s25072293 - 4 Apr 2025
Cited by 1 | Viewed by 772
Abstract
Specific emitter identification (SEI) methods based on deep learning (DL) have effectively addressed complex, multi-dimensional signal recognition tasks by leveraging deep neural networks. However, this advancement introduces challenges such as model parameter redundancy and high feature dimensionality, which pose limitations for resource-constrained (RC) [...] Read more.
Specific emitter identification (SEI) methods based on deep learning (DL) have effectively addressed complex, multi-dimensional signal recognition tasks by leveraging deep neural networks. However, this advancement introduces challenges such as model parameter redundancy and high feature dimensionality, which pose limitations for resource-constrained (RC) edge devices, especially in Internet of Things (IoT) applications. To tackle these problems, we propose an RC-SEI method based on efficient design and model compression. Specifically, for efficient design, we have developed a lightweight convolution network (LCNet) that aims to balance performance and complexity. Regarding model compression, we introduce sparse regularization techniques in the fully connected (FC) layer, achieving over 99% feature dimensionality reduction. Furthermore, we have comprehensively evaluated the proposed method on public automatic-dependent surveillance-broadcast (ADS-B) and Wi-Fi datasets. Simulation results demonstrate that our proposed method exhibits superior performance in terms of both recognition accuracy and model complexity. Specifically, LCNet achieved accuracies of 99.40% and 99.90% on the ADS-B and Wi-Fi datasets, respectively, with only 33,510 and 33,544 parameters. These results highlight the feasibility and potential of our proposed RC-SEI method for RC scenarios. Full article
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21 pages, 4965 KB  
Article
Specific Emitter Identification Method for Limited Samples via Time–Wavelet Spectrum Consistency
by Chunyang Tang, Jing Lian, Li Zheng and Rui Gao
Sensors 2025, 25(3), 648; https://doi.org/10.3390/s25030648 - 22 Jan 2025
Cited by 3 | Viewed by 1287
Abstract
Specific emitter identification (SEI) is a technique that identifies the emitter through physical layer features contained in radio signals, and it is widely used in tasks such as identifying illegal transmitters and authentication. Thanks to the development of deep learning, SEI tasks based [...] Read more.
Specific emitter identification (SEI) is a technique that identifies the emitter through physical layer features contained in radio signals, and it is widely used in tasks such as identifying illegal transmitters and authentication. Thanks to the development of deep learning, SEI tasks based on deep learning have achieved significant improvements in recognition performance. However, illegal transmitters often broadcast for short durations and at low frequencies, resulting in very limited available training samples. In such cases, directly training deep learning models may lead to underfitting issues, thereby reducing recognition accuracy. In this paper, to address the issue of traditional methods struggling to classify transmitters when there is a scarcity of emitter samples and limited training data, we propose a method based on TFC-CNN. We propose a TFC-CNN method. Specifically, we first use continuous wavelet transform (CWT) as a data augmentation method to construct time–wavelet spectrum sample pairs. Then, we use complex-valued neural networks (CVNNs) and deep convolutional neural networks (DCNNs) to extract the hidden emitter identity features from the time and wavelet spectrum samples. We train the model using the normalized temperature-scaled cross-entropy (NT-Xent) loss and cross-entropy (CE) loss, ensuring the consistency of feature vector distributions across the two modalities with cosine loss. Finally, we fine-tune the model to achieve SEI tasks with few samples. Experimental results on open-source WiFi datasets and automatic dependent surveillance–broadcast (ADS-B) datasets show that our proposed method outperforms existing state-of-the-art methods. With only 5% of the training samples, the recognition accuracy for the ADS-B test dataset is 84.10%, and for the WiFi test dataset, it is 96.99%. Full article
(This article belongs to the Section Communications)
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15 pages, 2427 KB  
Article
Specific Emitter Identification with Few-Shot via Deep Networks Based on Time and Frequency Domain with Channel, Spatial, and Self-Attention Mechanisms
by Yi Huang, Aiqun Hu, Lingyi Shi, Huifeng Tian, Jiayi Fan and Wei Ding
Electronics 2025, 14(1), 165; https://doi.org/10.3390/electronics14010165 - 3 Jan 2025
Viewed by 1095
Abstract
Specific emitter identification (SEI) is a highly active research area in physical layer security. In this paper, we propose a SEI scheme based on time-frequency domain channel, spatial, and self-attention mechanisms (TF-CSS) for deep networks with few-shot learning. The scheme first uses an [...] Read more.
Specific emitter identification (SEI) is a highly active research area in physical layer security. In this paper, we propose a SEI scheme based on time-frequency domain channel, spatial, and self-attention mechanisms (TF-CSS) for deep networks with few-shot learning. The scheme first uses an asymmetric masked auto-encoder (AMAE) with attention mechanisms for unsupervised learning, then removes the decoder and adds a linear layer as a classifier, and finally fine-tunes the whole network to achieve effective recognition. The scheme improves the feature representation and identification performance of complex-value neural network (CVNN)-based AMAE by adding channel, spatial, and self-attention mechanisms in the time-frequency domain, respectively. Experimental results show that this scheme outperforms the recognition accuracy of contrastive learning and other MAE/AMAE-based methods in 30 classes of LoRa baseband signal transmitter recognition with different few-shot scenarios and observation lengths. Full article
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16 pages, 5129 KB  
Article
Enhanced Electrochemiluminescence of Luminol and-Dissolved Oxygen by Nanochannel-Confined Au Nanomaterials for Sensitive Immunoassay of Carcinoembryonic Antigen
by Weibin Li, Ruliang Yu and Fengna Xi
Molecules 2024, 29(20), 4880; https://doi.org/10.3390/molecules29204880 - 15 Oct 2024
Cited by 9 | Viewed by 2156
Abstract
Simple development of an electrochemiluminescence (ECL) immunosensor for convenient detection of tumor biomarker is of great significance for early cancer diagnosis, treatment evaluation, and improving patient survival rates and quality of life. In this work, an immunosensor is demonstrated based on an enhanced [...] Read more.
Simple development of an electrochemiluminescence (ECL) immunosensor for convenient detection of tumor biomarker is of great significance for early cancer diagnosis, treatment evaluation, and improving patient survival rates and quality of life. In this work, an immunosensor is demonstrated based on an enhanced ECL signal boosted by nanochannel-confined Au nanomaterial, which enables sensitive detection of the tumor biomarker—carcinoembryonic antigen (CEA). Vertically-ordered mesoporous silica film (VMSF) with a nanochannel array and amine groups was rapidly grown on a simple and low-cost indium tin oxide (ITO) electrode using the electrochemically assisted self-assembly (EASA) method. Au nanomaterials were confined in situ on the VMSF through electrodeposition, which catalyzed both the conversion of dissolved oxygen (O2) to reactive oxygen species (ROS) and the oxidation of a luminol emitter and improved the electrode active surface. The ECL signal was enhanced fivefold after Au nanomaterial deposition. The recognitive interface was fabricated by covalent immobilization of the CEA antibody on the outer surface of the VMSF, followed with the blocking of non-specific binding sites. In the presence of CEA, the formed immunocomplex reduced the diffusion of the luminol emitter, resulting in the reduction of the ECL signal. Based on this mechanism, the constructed immunosensor was able to provide sensitive detection of CEA ranging from 1 pg·mL−1 to 100 ng·mL−1 with a low limit of detection (LOD, 0.37 pg·mL−1, S/N = 3). The developed immunosensor exhibited high selectivity and good stability. ECL determination of CEA in fetal bovine serum was achieved. Full article
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24 pages, 2630 KB  
Article
The Research of Intra-Pulse Modulated Signal Recognition of Radar Emitter under Few-Shot Learning Condition Based on Multimodal Fusion
by Yunhao Liu, Sicun Han, Chengjun Guo, Jiangyan Chen and Qing Zhao
Electronics 2024, 13(20), 4045; https://doi.org/10.3390/electronics13204045 - 14 Oct 2024
Cited by 1 | Viewed by 2051
Abstract
Radar radiation source recognition is critical for the reliable operation of radar communication systems. However, in increasingly complex electromagnetic environments, traditional identification methods face significant limitations. These methods often struggle with high noise levels and diverse modulation types, making it difficult to maintain [...] Read more.
Radar radiation source recognition is critical for the reliable operation of radar communication systems. However, in increasingly complex electromagnetic environments, traditional identification methods face significant limitations. These methods often struggle with high noise levels and diverse modulation types, making it difficult to maintain accuracy, especially when the Signal-to-Noise Ratio (SNR) is low or the available training data are limited. These difficulties are further intensified by the necessity to generalize in environments characterized by a substantial quantity of noisy, low-quality signal samples while being constrained by a limited number of desirable high-quality training samples. To more effectively address these issues, this paper proposes a novel approach utilizing Model-Agnostic Meta-Learning (MAML) to enhance model adaptability in few-shot learning scenarios, allowing the model to quickly learn with limited data and optimize parameters effectively. Furthermore, a multimodal fusion neural network, DCFANet, is designed, incorporating residual blocks, squeeze and excitation blocks, and a multi-scale CNN, to fuse I/Q waveform data and time–frequency image data for more comprehensive feature extraction. Our model enables more robust signal recognition, even when the signal quality is severely degraded by noise or when only a few examples of a signal type are available. Testing on 13 intra-pulse modulated signals in an Additive White Gaussian Noise (AWGN) environment across SNRs ranging from −20 to 10 dB demonstrated the approach’s effectiveness. Particularly, under a 5way5shot setting, the model achieves high classification accuracy even at −10dB SNR. Our research underscores the model’s ability to address the key challenges of radar emitter signal recognition in low-SNR and data-scarce conditions, demonstrating its strong adaptability and effectiveness in complex, real-world electromagnetic environments. Full article
(This article belongs to the Special Issue Digital Signal Processing and Wireless Communication)
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25 pages, 5900 KB  
Article
Progressive Unsupervised Domain Adaptation for Radio Frequency Signal Attribute Recognition across Communication Scenarios
by Jing Xiao, Hang Zhang, Zeqi Shao, Yikai Zheng and Wenrui Ding
Remote Sens. 2024, 16(19), 3696; https://doi.org/10.3390/rs16193696 - 4 Oct 2024
Cited by 1 | Viewed by 1411
Abstract
As the development of low-altitude economies and aerial countermeasures continues, the safety of unmanned aerial vehicles becomes increasingly critical, making emitter identification in remote sensing practices more essential. Effective recognition of radio frequency (RF) signal attributes is a prerequisite for identifying emitters. However, [...] Read more.
As the development of low-altitude economies and aerial countermeasures continues, the safety of unmanned aerial vehicles becomes increasingly critical, making emitter identification in remote sensing practices more essential. Effective recognition of radio frequency (RF) signal attributes is a prerequisite for identifying emitters. However, due to diverse wireless communication environments, RF signals often face challenges from complex and time-varying wireless channel conditions. These challenges lead to difficulties in data collection and annotation, as well as disparities in data distribution across different communication scenarios. To address this issue, this paper proposes a progressive maximum similarity-based unsupervised domain adaptation (PMS-UDA) method for RF signal attribute recognition. First, we introduce a noise perturbation consistency optimization method to enhance the robustness of the PMS-UDA method under low signal-to-noise conditions. Subsequently, a progressive label alignment training method is proposed, combining sample-level maximum correlation with distribution-level maximum similarity optimization techniques to enhance the similarity of cross-domain features. Finally, a domain adversarial optimization method is employed to extract domain-independent features, reducing the impact of channel scenarios. The experimental results demonstrate that the PMS-UDA method achieves superior recognition performance in automatic modulation recognition and RF fingerprint identification tasks, as well as across both ground-to-ground and air-to-ground scenarios, compared to baseline methods. Full article
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25 pages, 13951 KB  
Article
1D-CNN-Transformer for Radar Emitter Identification and Implemented on FPGA
by Xiangang Gao, Bin Wu, Peng Li and Zehuan Jing
Remote Sens. 2024, 16(16), 2962; https://doi.org/10.3390/rs16162962 - 12 Aug 2024
Cited by 3 | Viewed by 4473
Abstract
Deep learning has brought great development to radar emitter identification technology. In addition, specific emitter identification (SEI), as a branch of radar emitter identification, has also benefited from it. However, the complexity of most deep learning algorithms makes it difficult to adapt to [...] Read more.
Deep learning has brought great development to radar emitter identification technology. In addition, specific emitter identification (SEI), as a branch of radar emitter identification, has also benefited from it. However, the complexity of most deep learning algorithms makes it difficult to adapt to the requirements of the low power consumption and high-performance processing of SEI on embedded devices, so this article proposes solutions from the aspects of software and hardware. From the software side, we design a Transformer variant network, lightweight convolutional Transformer (LW-CT) that supports parameter sharing. Then, we cascade convolutional neural networks (CNNs) and the LW-CT to construct a one-dimensional-CNN-Transformer(1D-CNN-Transformer) lightweight neural network model that can capture the long-range dependencies of radar emitter signals and extract signal spatial domain features meanwhile. In terms of hardware, we design a low-power neural network accelerator based on an FPGA to complete the real-time recognition of radar emitter signals. The accelerator not only designs high-efficiency computing engines for the network, but also devises a reconfigurable buffer called “Ping-pong CBUF” and two-level pipeline architecture for the convolution layer for alleviating the bottleneck caused by the off-chip storage access bandwidth. Experimental results show that the algorithm can achieve a high recognition performance of SEI with a low calculation overhead. In addition, the hardware acceleration platform not only perfectly meets the requirements of the radar emitter recognition system for low power consumption and high-performance processing, but also outperforms the accelerators in other papers in terms of the energy efficiency ratio of Transformer layer processing. Full article
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22 pages, 937 KB  
Article
Radar Emitter Recognition Based on Spiking Neural Networks
by Zhenghao Luo, Xingdong Wang, Shuo Yuan and Zhangmeng Liu
Remote Sens. 2024, 16(14), 2680; https://doi.org/10.3390/rs16142680 - 22 Jul 2024
Cited by 3 | Viewed by 2513
Abstract
Efficient and effective radar emitter recognition is critical for electronic support measurement (ESM) systems. However, in complex electromagnetic environments, intercepted pulse trains generally contain substantial data noise, including spurious and missing pulses. Currently, radar emitter recognition methods utilizing traditional artificial neural networks (ANNs) [...] Read more.
Efficient and effective radar emitter recognition is critical for electronic support measurement (ESM) systems. However, in complex electromagnetic environments, intercepted pulse trains generally contain substantial data noise, including spurious and missing pulses. Currently, radar emitter recognition methods utilizing traditional artificial neural networks (ANNs) like CNNs and RNNs are susceptible to data noise and require intensive computations, posing challenges to meeting the performance demands of modern ESM systems. Spiking neural networks (SNNs) exhibit stronger representational capabilities compared to traditional ANNs due to the temporal dynamics of spiking neurons and richer information encoded in precise spike timing. Furthermore, SNNs achieve higher computational efficiency by performing event-driven sparse addition calculations. In this paper, a lightweight spiking neural network is proposed by combining direct coding, leaky integrate-and-fire (LIF) neurons, and surrogate gradients to recognize radar emitters. Additionally, an improved SNN for radar emitter recognition is proposed, leveraging the local timing structure of pulses to enhance adaptability to data noise. Simulation results demonstrate the superior performance of the proposed method over existing methods. Full article
(This article belongs to the Special Issue Technical Developments in Radar—Processing and Application)
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22 pages, 5307 KB  
Article
Transfer Learning-Based Specific Emitter Identification for ADS-B over Satellite System
by Mingqian Liu, Yae Chai, Ming Li, Jiakun Wang and Nan Zhao
Remote Sens. 2024, 16(12), 2068; https://doi.org/10.3390/rs16122068 - 7 Jun 2024
Cited by 11 | Viewed by 1797
Abstract
In future aviation surveillance, the demand for higher real-time updates for global flights can be met by deploying automatic dependent surveillance–broadcast (ADS-B) receivers on low Earth orbit satellites, capitalizing on their global coverage and terrain-independent capabilities for seamless monitoring. Specific emitter identification (SEI) [...] Read more.
In future aviation surveillance, the demand for higher real-time updates for global flights can be met by deploying automatic dependent surveillance–broadcast (ADS-B) receivers on low Earth orbit satellites, capitalizing on their global coverage and terrain-independent capabilities for seamless monitoring. Specific emitter identification (SEI) leverages the distinctive features of ADS-B data. High data collection and annotation costs, along with limited dataset size, can lead to overfitting during training and low model recognition accuracy. Transfer learning, which does not require source and target domain data to share the same distribution, significantly reduces the sensitivity of traditional models to data volume and distribution. It can also address issues related to the incompleteness and inadequacy of communication emitter datasets. This paper proposes a distributed sensor system based on transfer learning to address the specific emitter identification. Firstly, signal fingerprint features are extracted using a bispectrum transform (BST) to train a convolutional neural network (CNN) preliminarily. Decision fusion is employed to tackle the challenges of the distributed system. Subsequently, a transfer learning strategy is employed, incorporating frozen model parameters, maximum mean discrepancy (MMD), and classification error measures to reduce the disparity between the target and source domains. A hyperbolic space module is introduced before the output layer to enhance the expressive capacity and data information extraction. After iterative training, the transfer learning model is obtained. Simulation results confirm that this method enhances model generalization, addresses the issue of slow convergence, and leads to improved training accuracy. Full article
(This article belongs to the Section Engineering Remote Sensing)
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23 pages, 7234 KB  
Article
Attention-Enhanced Dual-Branch Residual Network with Adaptive L-Softmax Loss for Specific Emitter Identification under Low-Signal-to-Noise Ratio Conditions
by Zehuan Jing, Peng Li, Bin Wu, Erxing Yan, Yingchao Chen and Youbing Gao
Remote Sens. 2024, 16(8), 1332; https://doi.org/10.3390/rs16081332 - 10 Apr 2024
Cited by 6 | Viewed by 1677
Abstract
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 [...] Read more.
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%. Full article
(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)
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30 pages, 14249 KB  
Review
Intelligent, Flexible Artificial Throats with Sound Emitting, Detecting, and Recognizing Abilities
by Junxin Fu, Zhikang Deng, Chang Liu, Chuting Liu, Jinan Luo, Jingzhi Wu, Shiqi Peng, Lei Song, Xinyi Li, Minli Peng, Houfang Liu, Jianhua Zhou and Yancong Qiao
Sensors 2024, 24(5), 1493; https://doi.org/10.3390/s24051493 - 25 Feb 2024
Cited by 5 | Viewed by 4163
Abstract
In recent years, there has been a notable rise in the number of patients afflicted with laryngeal diseases, including cancer, trauma, and other ailments leading to voice loss. Currently, the market is witnessing a pressing demand for medical and healthcare products designed to [...] Read more.
In recent years, there has been a notable rise in the number of patients afflicted with laryngeal diseases, including cancer, trauma, and other ailments leading to voice loss. Currently, the market is witnessing a pressing demand for medical and healthcare products designed to assist individuals with voice defects, prompting the invention of the artificial throat (AT). This user-friendly device eliminates the need for complex procedures like phonation reconstruction surgery. Therefore, in this review, we will initially give a careful introduction to the intelligent AT, which can act not only as a sound sensor but also as a thin-film sound emitter. Then, the sensing principle to detect sound will be discussed carefully, including capacitive, piezoelectric, electromagnetic, and piezoresistive components employed in the realm of sound sensing. Following this, the development of thermoacoustic theory and different materials made of sound emitters will also be analyzed. After that, various algorithms utilized by the intelligent AT for speech pattern recognition will be reviewed, including some classical algorithms and neural network algorithms. Finally, the outlook, challenge, and conclusion of the intelligent AT will be stated. The intelligent AT presents clear advantages for patients with voice impairments, demonstrating significant social values. Full article
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17 pages, 2757 KB  
Article
Research on an Enhanced Multimodal Network for Specific Emitter Identification
by Heli Peng, Kai Xie and Wenxu Zou
Electronics 2024, 13(3), 651; https://doi.org/10.3390/electronics13030651 - 4 Feb 2024
Cited by 3 | Viewed by 1754
Abstract
Specific emitter identification (SEI) refers to the task of distinguishing similar emitters, especially those of the same type and transmission parameters, which is one of the most critical tasks of electronic warfare. However, SEI is still a challenging task when a feature has [...] Read more.
Specific emitter identification (SEI) refers to the task of distinguishing similar emitters, especially those of the same type and transmission parameters, which is one of the most critical tasks of electronic warfare. However, SEI is still a challenging task when a feature has low physical representation. Feature representation largely determines the recognition results. Therefore, this article expects to move toward robust feature representation for SEI. Efficient multimodal strategies have great potential for applications using multimodal data and can further improve the performance of SEI. In this research, we introduce a multimodal emitter identification method that explores the application of multimodal data, time-series radar signals, and feature vector data to an enhanced transformer, which employs a conformer block to embed the raw data and integrates an efficient multimodal feature representation module. Moreover, we employ self-knowledge distillation to mitigate overconfident predictions and reduce intra-class variations. Our study reveals that multimodal data provide sufficient information for specific emitter identification. Simultaneously, we propose the CV-CutMixOut method to augment the time-domain signal. Extensive experiments on real radar datasets indicate that the proposed method achieves more accurate identification results and higher feature discriminability. Full article
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20 pages, 8200 KB  
Article
Efficient FPGA Implementation of Convolutional Neural Networks and Long Short-Term Memory for Radar Emitter Signal Recognition
by Bin Wu, Xinyu Wu, Peng Li, Youbing Gao, Jiangbo Si and Naofal Al-Dhahir
Sensors 2024, 24(3), 889; https://doi.org/10.3390/s24030889 - 30 Jan 2024
Cited by 11 | Viewed by 3979
Abstract
In recent years, radar emitter signal recognition has enjoyed a wide range of applications in electronic support measure systems and communication security. More and more deep learning algorithms have been used to improve the recognition accuracy of radar emitter signals. However, complex deep [...] Read more.
In recent years, radar emitter signal recognition has enjoyed a wide range of applications in electronic support measure systems and communication security. More and more deep learning algorithms have been used to improve the recognition accuracy of radar emitter signals. However, complex deep learning algorithms and data preprocessing operations have a huge demand for computing power, which cannot meet the requirements of low power consumption and high real-time processing scenarios. Therefore, many research works have remained in the experimental stage and cannot be actually implemented. To tackle this problem, this paper proposes a resource reuse computing acceleration platform based on field programmable gate arrays (FPGA), and implements a one-dimensional (1D) convolutional neural network (CNN) and long short-term memory (LSTM) neural network (NN) model for radar emitter signal recognition, directly targeting the intermediate frequency (IF) data of radar emitter signal for classification and recognition. The implementation of the 1D-CNN-LSTM neural network on FPGA is realized by multiplexing the same systolic array to accomplish the parallel acceleration of 1D convolution and matrix vector multiplication operations. We implemented our network on Xilinx XCKU040 to evaluate the effectiveness of our proposed solution. Our experiments show that the system can achieve 7.34 giga operations per second (GOPS) data throughput with only 5.022 W power consumption when the radar emitter signal recognition rate is 96.53%, which greatly improves the energy efficiency ratio and real-time performance of the radar emitter recognition system. Full article
(This article belongs to the Special Issue Radar Signal Detection, Recognition and Identification)
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25 pages, 29121 KB  
Article
Radar Emitter Signal Intra-Pulse Modulation Open Set Recognition Based on Deep Neural Network
by Shibo Yuan, Peng Li and Bin Wu
Remote Sens. 2024, 16(1), 108; https://doi.org/10.3390/rs16010108 - 26 Dec 2023
Cited by 8 | Viewed by 2159
Abstract
Radar emitter signal intra-pulse modulation recognition is important for modern electronic reconnaissance systems to analyze target radar systems. In the actual environment, the intra-pulse modulations of the sampled radar emitter signals contain not only the known types in the library but also the [...] Read more.
Radar emitter signal intra-pulse modulation recognition is important for modern electronic reconnaissance systems to analyze target radar systems. In the actual environment, the intra-pulse modulations of the sampled radar emitter signals contain not only the known types in the library but also the unknown types. Therefore, the existing recognition methods, which are based on a closed set, cannot recognize the unknown samples. In order to solve this problem, in this paper, we proposed a method for radar emitter signal intra-pulse modulation open set recognition. The proposed method could classify the known modulations and identify the unknown modulation by using an original deep neural network-based recognition model trained on a closed set, estimating the signal-to-noise ratio, and calculating the reconstruction loss by an encoder–decoder model. For a given sample, the original deep neural network-based recognition model will label it as a certain known class temporarily. By estimating the SNR of the sample and calculating the reconstruction loss by inputting the sample to the corresponding encoder–decoder model related to the temporary predicted known class, whether the sample belongs to the predicted temporary known class or the unknown class will be confirmed. Experiments were conducted with five different openness conditions. The experimental results indicate that the proposed method has good performance on radar emitter signal intra-pulse modulation open set recognition. Full article
(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)
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16 pages, 3098 KB  
Article
The Fabrication of a Probe-Integrated Electrochemiluminescence Aptasensor Based on Double-Layered Nanochannel Array with Opposite Charges for the Sensitive Determination of C-Reactive Protein
by Feng Li, Qianqian Han and Fengna Xi
Molecules 2023, 28(23), 7867; https://doi.org/10.3390/molecules28237867 - 30 Nov 2023
Cited by 18 | Viewed by 1769
Abstract
The effective and sensitive detection of the important biomarker, C-reactive protein (CRP), is of great significance in clinical diagnosis. The development of a convenient and highly sensitive electrochemiluminescence (ECL) aptasensor with an immobilized emitter probe is highly desirable. In this work, a probe-integrated [...] Read more.
The effective and sensitive detection of the important biomarker, C-reactive protein (CRP), is of great significance in clinical diagnosis. The development of a convenient and highly sensitive electrochemiluminescence (ECL) aptasensor with an immobilized emitter probe is highly desirable. In this work, a probe-integrated ECL aptamer sensor was constructed based on a bipolar silica nanochannel film (bp-SNF) modified electrode for the highly sensitive ECL detection of CRP. The bp-SNF, modified on an ITO electrode, consisted of a dual-layered SNF film, including the negatively charged inner SNF (n-SNF) and the outer SNF with a positive charge and amino groups (p-SNF). The ECL emitter, tris(bipyridine) ruthenium (II) (Ru(bpy)32+), was stably immobilized in a nanochannel of bp-SNF using the dual electrostatic interactions with n-SNF attracting and p-SNF repelling. The amino groups on the outer surface of bp-SNF were aldehyde derivatized, allowing for the covalent immobilization of recognitive aptamers (5′-NH2-CGAAGGGGATTCGAGGGGTGATTGCGTGCTCCATTTGGTG-3′), leading to the recognition interface. When CRP bound to the aptamer on the recognition interface, the formed complex increased the interface resistance and reduced the diffusion of the co-reactant tripropylamine (TPA) into the nanochannels, leading to a decrease in the ECL signal. Based on this mechanism, the constructed aptamer sensor could achieve a sensitive ECL detection of CRP ranging from 0.01 to 1000 ng/mL, with a detection limit (DL) of 8.5 pg/mL. The method for constructing this probe-integrated ECL aptamer sensor is simple, and it offers a high probe stability, good selectivity, and high sensitivity. Full article
(This article belongs to the Special Issue Biosensors for Molecules Detection)
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