Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning

Chen, Kuiyu; Zhang, Shuning; Zhu, Lingzhi; Chen, Si; Zhao, Huichang

doi:10.3390/s21020449

Cited by 22 publications

(10 citation statements)

References 34 publications

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“…In this study, the Backpropagation Neural Network (BPNN) approach was implemented for the purpose of classification. Chen et al [18] presented a model based on the intrapulse signatures of the radar signals utilizing both Adaptive Singular Value Reconstruction (ASVR) as well as deep residual learning. Primarily, the time-frequency spectrum of the radar signals, in minimal SNRs, has increased the denoising processes of the ASVR model.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Modulation Recognition of Communication Signal for Next-Generation 6G Networks

Alnfiai¹

2023

Computers, Materials &Amp; Continua

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In recent years, the need for a fast, efficient and a reliable wireless network has increased dramatically. Numerous 5G networks have already been tested while a few are in the early stages of deployment. In noncooperative communication scenarios, the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over them. The recent advancements in both Machine Learning (ML) and Deep Learning (DL) models demand the development of effective modulation recognition models with self-learning capability. In this background, the current research article designs a Deep Learning enabled Intelligent Modulation Recognition of Communication Signal (DLIMR-CS) technique for next-generation networks. The aim of the proposed DLIMR-CS technique is to classify different kinds of digitally-modulated signals. In addition, the fractal feature extraction process is applied with the help of the Sevcik Fractal Dimension (SFD) approach. Then, the extracted features are fed into the Deep Variational Autoencoder (DVAE) model for the classification of the modulated signals. In order to improve the classification performance of the DVAE model, the Tunicate Swarm Algorithm (TSA) is used to finetune the hyperparameters involved in DVAE model. A wide range of simulations was conducted to establish the enhanced performance of the proposed DLIMR-CS model. The experimental outcomes confirmed the superior recognition rate of the DLIMR-CS model over recent state-of-the-art methods under different evaluation parameters.

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Section: Related Workmentioning

confidence: 99%

Intelligent Modulation Recognition of Communication Signal for Next-Generation 6G Networks

Alnfiai¹

2023

Computers, Materials &Amp; Continua

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“…To overcome the limitations of current methods, this paper aims to present a novel approach specifically designed to operate effectively under low SNR conditions. Successful signal recognition in such scenarios necessitates the incorporation of noise suppression techniques [18][19][20]. The studies referenced in [19,21] employ additional denoising techniques, which can increase hardware complexity and system response time.…”

Section: Introductionmentioning

confidence: 99%

“…Successful signal recognition in such scenarios necessitates the incorporation of noise suppression techniques [18][19][20]. The studies referenced in [19,21] employ additional denoising techniques, which can increase hardware complexity and system response time. Higher-order spectra, such as the bispectrum, have exhibited effectiveness in noise suppression while preserving rich signal characteristics.…”

Section: Introductionmentioning

confidence: 99%

Modulation Recognition of Low-SNR UAV Radar Signals Based on Bispectral Slices and GA-BP Neural Network

Liu

Song

Chen

et al. 2023

Drones

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In this paper, we address the challenge of low recognition rates in existing methods for radar signals from unmanned aerial vehicles (UAV) with low signal-to-noise ratios (SNRs). To overcome this challenge, we propose the utilization of the bispectral slice approach for accurate recognition of complex UAV radar signals. Our approach involves extracting the bispectral diagonal slice and the maximum bispectral amplitude horizontal slice from the bispectrum amplitude spectrum of the received UAV radar signal. These slices serve as the basis for subsequent identification by calculating characteristic parameters such as convexity, box dimension, and sparseness. To accomplish the recognition task, we employ a GA-BP neural network. The significant variations observed in the bispectral slices of different signals, along with their robustness against Gaussian noise, contribute to the high separability and stability of the extracted bispectral convexity, bispectral box dimension, and bispectral sparseness. Through simulations involving five radar signals, our proposed method demonstrates superior performance. Remarkably, even under challenging conditions with an SNR as low as −3 dB, the recognition accuracy for the five different radar signals exceeds 90%. Our research aims to enhance the understanding and application of modulation recognition techniques for UAV radar signals, particularly in scenarios with low SNRs.

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“…It is difficult to analyze different kinds of signal characteristics. As a result, deep learning is applied in the signal processing field with powerful optimization and learning abilities, such as signal recognition [ 6 ], blind equalization [ 7 ], and spectrum sensing [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Radio Frequency Database Construction and Modulation Recognition in Wireless Sensor Networks

Liu

Xiang

Yin

2022

Sensors

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Current modulation recognition methods in wireless sensor networks rely too much on simulation datasets. Its practical application effect cannot reach the expected results. To address this issue, in this paper we collect a large amount of real-world wireless signal data based on the software radio device USRP 2920. We then propose a real radio frequency (RF) database architecture and preprocessing operators to manage real-world wireless signal data, conduct signal preprocessing, and export the dataset. Based on different feature datasets derived from the RF database, we propose a multidimensional feature hybrid network (MFHN), which is used to identify unknown signals by analyzing different kinds of signal features. Further, we improve MFHN and design a multifeatured joint migration network (MJMN) to identify small-sample targets. The experimental results show that the recognition rates for unknown target signals of the MFHN and MJMN are 82.7% and 93.2%, respectively. The proposed methods improve the recognition performance in the single node of wireless sensor networks in complex electromagnetic environments, which provides reference for subsequent decision fusion.

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Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning

Cited by 22 publications

References 34 publications

Intelligent Modulation Recognition of Communication Signal for Next-Generation 6G Networks

Intelligent Modulation Recognition of Communication Signal for Next-Generation 6G Networks

Modulation Recognition of Low-SNR UAV Radar Signals Based on Bispectral Slices and GA-BP Neural Network

Radio Frequency Database Construction and Modulation Recognition in Wireless Sensor Networks

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