Digital Signal Processing最新文献

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Radio jamming recognition algorithm based on MS-SSA and the CSA-CNN

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-01-22 DOI: 10.1016/j.dsp.2025.105019

Xiaowen Cai, Pinchun Li, Mingyuan Liu, Yangzhuo Chen, Jiajia Lu

The application of wireless communication systems is continuously increasing across various fields. However, due to complex electromagnetic interference, these systems struggle to transmit data accurately, particularly in environments where Global Navigation Satellite Systems (GNSS) are unavailable. To ensure the accuracy of positioning information, it is essential to research antijamming techniques. Interference identification serves as a prerequisite for effective antijamming strategies. This paper proposes a jamming recognition algorithm based on multistep singular spectrum analysis (MS-SSA) and the channel-spatial attention convolutional neural network (CSA-CNN). Noisy jamming signals are filtered via the MS-SSA method to enhance the characteristics of jamming signals at low jamming-to-noise ratios (JNRs). After filtering, the CSA-CNN is employed for jamming recognition, incorporating multidomain feature parameters. The CSA-CNN integrates the global attention mechanism to enhance the model's ability to address significant jamming features, thereby improving recognition performance. The experimental results indicate that MS-SSA achieves a superior filtering effect compared with conventional methods such as the wavelet and Kalman algorithms. In identifying jamming signals, the recognition accuracy of the CSA-CNN can exceed 90% at JNR=-2 dB. The CSA-CNN achieves superior recognition performance and generalizability compared to the convolutional neural network (CNN) and multi-branch CNN (MB-CNN).

{"title":"Radio jamming recognition algorithm based on MS-SSA and the CSA-CNN","authors":"Xiaowen Cai, Pinchun Li, Mingyuan Liu, Yangzhuo Chen, Jiajia Lu","doi":"10.1016/j.dsp.2025.105019","DOIUrl":"10.1016/j.dsp.2025.105019","url":null,"abstract":"<div><div>The application of wireless communication systems is continuously increasing across various fields. However, due to complex electromagnetic interference, these systems struggle to transmit data accurately, particularly in environments where Global Navigation Satellite Systems (GNSS) are unavailable. To ensure the accuracy of positioning information, it is essential to research antijamming techniques. Interference identification serves as a prerequisite for effective antijamming strategies. This paper proposes a jamming recognition algorithm based on multistep singular spectrum analysis (MS-SSA) and the channel-spatial attention convolutional neural network (CSA-CNN). Noisy jamming signals are filtered via the MS-SSA method to enhance the characteristics of jamming signals at low jamming-to-noise ratios (JNRs). After filtering, the CSA-CNN is employed for jamming recognition, incorporating multidomain feature parameters. The CSA-CNN integrates the global attention mechanism to enhance the model's ability to address significant jamming features, thereby improving recognition performance. The experimental results indicate that MS-SSA achieves a superior filtering effect compared with conventional methods such as the wavelet and Kalman algorithms. In identifying jamming signals, the recognition accuracy of the CSA-CNN can exceed 90% at JNR=-2 dB. The CSA-CNN achieves superior recognition performance and generalizability compared to the convolutional neural network (CNN) and multi-branch CNN (MB-CNN).</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 105019"},"PeriodicalIF":2.9,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143144283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient hardware implementations of trigonometric functions and their application to sine-based modified logistic map

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-01-21 DOI: 10.1016/j.dsp.2025.104993

Sara M. Mohamed , Mohammed H. Yacoub , Wafaa S. Sayed , Lobna A. Said , Ahmed G. Radwan

Trigonometric functions' efficient realization is essential for accurate computations in various applications, including chaotic systems that have a highly error-sensitivity nature. This paper studies the FPGA implementations of the trigonometric functions employing several methods, including LUT-based, polynomial approximation, and CORDIC. Additionally, it proposes a novel sine-based enhanced modified logistic map (EMLM) using the sine chaotification method and studies its chaotic properties using a bifurcation diagram. The trigonometric implementations are utilized to realize the sine chaotic map and the EMLM. Performance analyses are performed for the sine map and EMLM implementations, including time series, frequency distribution, histogram, and 0-1 test. The 0-1 test gives the best results for the chaotic maps that employed CORDIC realization. The FPGA resources are evaluated for all trigonometric implementations, indicating the hardware efficiency and suitability of the proposed implementations in potential applications. The chaotic maps implementations achieve a throughput of up to 0.78 Gbit/s in the polynomial-based Sine maps, demonstrating their high-performance capabilities while reaching an accurate solution.

{"title":"Efficient hardware implementations of trigonometric functions and their application to sine-based modified logistic map","authors":"Sara M. Mohamed , Mohammed H. Yacoub , Wafaa S. Sayed , Lobna A. Said , Ahmed G. Radwan","doi":"10.1016/j.dsp.2025.104993","DOIUrl":"10.1016/j.dsp.2025.104993","url":null,"abstract":"<div><div>Trigonometric functions' efficient realization is essential for accurate computations in various applications, including chaotic systems that have a highly error-sensitivity nature. This paper studies the FPGA implementations of the trigonometric functions employing several methods, including LUT-based, polynomial approximation, and CORDIC. Additionally, it proposes a novel sine-based enhanced modified logistic map (EMLM) using the sine chaotification method and studies its chaotic properties using a bifurcation diagram. The trigonometric implementations are utilized to realize the sine chaotic map and the EMLM. Performance analyses are performed for the sine map and EMLM implementations, including time series, frequency distribution, histogram, and 0-1 test. The 0-1 test gives the best results for the chaotic maps that employed CORDIC realization. The FPGA resources are evaluated for all trigonometric implementations, indicating the hardware efficiency and suitability of the proposed implementations in potential applications. The chaotic maps implementations achieve a throughput of up to 0.78 Gbit/s in the polynomial-based Sine maps, demonstrating their high-performance capabilities while reaching an accurate solution.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 104993"},"PeriodicalIF":2.9,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parameter identification strategy for fractional-order hammerstein MIMO systems with PEMFC experimental validation

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-01-21 DOI: 10.1016/j.dsp.2025.105024

Chunlei Liu , Hongwei Wang , Qian Zhang

This study introduces a four-stage method for parameter identification in non-homogeneous fractional-order Hammerstein MIMO systems, which solves the problems of difficulty in determining the initial values of parameters and high computational complexity. The method gradually shifts from homogeneous models to non-homogeneous models, which enhances the convergence stability of the algorithm and reduces the computational complexity. Initially, the system is simplified to a single homogeneous model, then each output subsystem is treated as homogeneous. Non-homogeneous characteristics are introduced in the third stage, and by the fourth, the entire system is considered non-homogeneous. This gradual refinement avoids the complexity of determining the initial values of the fractional order. The improved Levenberg-Marquardt algorithm, combined with the multi-innovation principle, enhances identification accuracy and global search performance. A numerical example and a PEMFC experiment verify the effectiveness and the superiority of the method.

引用次数: 0

Joint source localization and propagation speed estimation using TDOA with hypothesized propagation speed

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-01-21 DOI: 10.1016/j.dsp.2024.104934

Shaohong Xu, Minghai Yang, Chengyu Li, Beichuan Tang, Yanbing Yang, Liangyin Chen, Yimao Sun

Underwater acoustic localization (UWAL) presents a significant challenge in numerous underwater applications. The speed of sound propagation, often treated as an unknown parameter, varies across different underwater environments, necessitating the joint estimation of both the source position and the sound propagation speed. In this study, we model the sound propagation speed as the sum of a hypothesized constant and a residual term, thereby formulating a new optimization problem to determine the source position and the residual speed. To address the rank-deficient issue, we employ a nullspace projection, enabling an coarse estimate of the source position through weighted least squares (WLS). To enhance accuracy, two strategies lead to two methods: the first utilizes perturbation analysis to estimate a correction that reduces error, while the second refines the coarse estimate using the maximum likelihood objective function and Taylor expansion. Performance analysis demonstrates that both proposed methods can achieve the Cramér–Rao lower bound (CRLB) in low-noise conditions. Simulations validate these analytical results and highlight the computational efficiency of the proposed methods.

{"title":"Joint source localization and propagation speed estimation using TDOA with hypothesized propagation speed","authors":"Shaohong Xu, Minghai Yang, Chengyu Li, Beichuan Tang, Yanbing Yang, Liangyin Chen, Yimao Sun","doi":"10.1016/j.dsp.2024.104934","DOIUrl":"10.1016/j.dsp.2024.104934","url":null,"abstract":"<div><div>Underwater acoustic localization (UWAL) presents a significant challenge in numerous underwater applications. The speed of sound propagation, often treated as an unknown parameter, varies across different underwater environments, necessitating the joint estimation of both the source position and the sound propagation speed. In this study, we model the sound propagation speed as the sum of a hypothesized constant and a residual term, thereby formulating a new optimization problem to determine the source position and the residual speed. To address the rank-deficient issue, we employ a nullspace projection, enabling an coarse estimate of the source position through weighted least squares (WLS). To enhance accuracy, two strategies lead to two methods: the first utilizes perturbation analysis to estimate a correction that reduces error, while the second refines the coarse estimate using the maximum likelihood objective function and Taylor expansion. Performance analysis demonstrates that both proposed methods can achieve the Cramér–Rao lower bound (CRLB) in low-noise conditions. Simulations validate these analytical results and highlight the computational efficiency of the proposed methods.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 104934"},"PeriodicalIF":2.9,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Weak celestial source fringes detection based on multi-task learning and pseudo soft threshold residual denoising network

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-01-21 DOI: 10.1016/j.dsp.2025.105014

Ruiqing Yan , Zongyao Yin , Cong Dai , Wengping Qi , Xiaojin Shi , Dan Hu , Dan Wu , Xianchuan Yu

Detecting low signal-to-noise ratios weak celestial source signals from large volumes of astronomical radio data is a significant and challenging task. Current mainstream approaches predominantly rely on manual processing, resulting in low efficiency. While a few deep learning-based methods have been applied, they typically utilize generic techniques, leading to suboptimal detection accuracy. To address these challenges, this paper proposes a novel method for detecting weak celestial source signals, tailored to the unique characteristics of celestial source data. The method integrates multi-task learning with pseudo-soft threshold residual denoising. Firstly, transfer learning is introduced to leverage a pre-trained model for extracting features from celestial source fringes and performing signal recognition. Multi-task learning is employed to enhance detection efficiency and reduce the false detection rate. Secondly, a novel pseudo-soft threshold function is proposed, and a corresponding pseudo-soft threshold residual denoising network is developed to automatically learn the optimal threshold and eliminate noise features. Additionally, a multi-layer fusion feature pyramid network is proposed to improve the extraction of features from weak celestial source fringes. Simulated data, generated based on the parameters of the Tianlai radio telescope observation system, is used to construct a training dataset. The performance of the proposed algorithm is evaluated using both simulated and real observational data. Experimental results demonstrate that the proposed method achieves satisfactory recognition accuracy, providing significant benefits for astronomers in detecting weak celestial source signals from extensive radio observation data. The code of this work will be available at https://github.com/YanRuiqing/MTL-PSTRD to facilitate reproducibility.

{"title":"Weak celestial source fringes detection based on multi-task learning and pseudo soft threshold residual denoising network","authors":"Ruiqing Yan , Zongyao Yin , Cong Dai , Wengping Qi , Xiaojin Shi , Dan Hu , Dan Wu , Xianchuan Yu","doi":"10.1016/j.dsp.2025.105014","DOIUrl":"10.1016/j.dsp.2025.105014","url":null,"abstract":"<div><div>Detecting low signal-to-noise ratios weak celestial source signals from large volumes of astronomical radio data is a significant and challenging task. Current mainstream approaches predominantly rely on manual processing, resulting in low efficiency. While a few deep learning-based methods have been applied, they typically utilize generic techniques, leading to suboptimal detection accuracy. To address these challenges, this paper proposes a novel method for detecting weak celestial source signals, tailored to the unique characteristics of celestial source data. The method integrates multi-task learning with pseudo-soft threshold residual denoising. Firstly, transfer learning is introduced to leverage a pre-trained model for extracting features from celestial source fringes and performing signal recognition. Multi-task learning is employed to enhance detection efficiency and reduce the false detection rate. Secondly, a novel pseudo-soft threshold function is proposed, and a corresponding pseudo-soft threshold residual denoising network is developed to automatically learn the optimal threshold and eliminate noise features. Additionally, a multi-layer fusion feature pyramid network is proposed to improve the extraction of features from weak celestial source fringes. Simulated data, generated based on the parameters of the Tianlai radio telescope observation system, is used to construct a training dataset. The performance of the proposed algorithm is evaluated using both simulated and real observational data. Experimental results demonstrate that the proposed method achieves satisfactory recognition accuracy, providing significant benefits for astronomers in detecting weak celestial source signals from extensive radio observation data. The code of this work will be available at <span><span>https://github.com/YanRuiqing/MTL-PSTRD</span><svg><path></path></svg></span> to facilitate reproducibility.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 105014"},"PeriodicalIF":2.9,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Trust-aware filtering in the presence of non-Gaussian noise and cyber attacks

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-01-21 DOI: 10.1016/j.dsp.2025.105008

Weiqin Dong , Junliang Lu , Gang Wang , Ying Zhang

Cyber attacks and non-Gaussian noise interference present unique security challenges to Wireless Sensor Networks (WSNs). Despite the existence of many techniques to resist non-Gaussian noise and attacks, when the system is simultaneously affected by both, non-Gaussian noise can, to some extent, affect attack detection and mitigation, leading to the failure of attack detection and degradation of system performance. In this paper, we propose a trust-based distributed Kalman filtering technique. We introduce a new trust evaluation metric combined with clustering methods for identifying attacked nodes. The maximum correntropy Kalman filter (MCKF) is employed for information fusion to mitigate the effects of non-Gaussian noise. Additionally, a malicious detection mechanism based on trust metrics' similarity is proposed. Compared to recently proposed trust-based methods, simulation results demonstrate that the proposed filter can simultaneously resist non-Gaussian noise interference and cyber attacks, with better performance.

引用次数: 0

Wind turbine blade fault detection based on graph Fourier transform and deep learning

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-01-21 DOI: 10.1016/j.dsp.2025.105007

Xiang Pan , Andi Chen , Chenhui Zhang , Junxiong Wang , Jie Zhou , Weize Xu

Wind turbine blades play an important role in harnessing wind power to generate electricity. And they are susceptible to damage due to fatigue loads and exposure to harsh operating environments. Thus, early warning of the damage of wind turbine blades is vital for reducing maintenance costs but in face of challenge of weak signal detection. A spatial-temporal joint processing framework is proposed based on combination of graph Fourier transform and deep learning for detection of wind turbine blade faults. The microphone array processing is utilized to enhance the weak abnormal signal emitted by the damaged wind turbine blades. Then the enhanced signal is projected from time domain into graph domain by graph Fourier transform. And short time Graph Fourier transform (STGFT) features and graph Mel filter banks (Gbank) features are extracted from graph domain. Finally, an advanced deep neural network is designed to extract deep semantic features from the graph signal. The experimental results have validated the effectiveness of the deep learning based fault detection methodology.

{"title":"Wind turbine blade fault detection based on graph Fourier transform and deep learning","authors":"Xiang Pan , Andi Chen , Chenhui Zhang , Junxiong Wang , Jie Zhou , Weize Xu","doi":"10.1016/j.dsp.2025.105007","DOIUrl":"10.1016/j.dsp.2025.105007","url":null,"abstract":"<div><div>Wind turbine blades play an important role in harnessing wind power to generate electricity. And they are susceptible to damage due to fatigue loads and exposure to harsh operating environments. Thus, early warning of the damage of wind turbine blades is vital for reducing maintenance costs but in face of challenge of weak signal detection. A spatial-temporal joint processing framework is proposed based on combination of graph Fourier transform and deep learning for detection of wind turbine blade faults. The microphone array processing is utilized to enhance the weak abnormal signal emitted by the damaged wind turbine blades. Then the enhanced signal is projected from time domain into graph domain by graph Fourier transform. And short time Graph Fourier transform (STGFT) features and graph Mel filter banks (Gbank) features are extracted from graph domain. Finally, an advanced deep neural network is designed to extract deep semantic features from the graph signal. The experimental results have validated the effectiveness of the deep learning based fault detection methodology.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 105007"},"PeriodicalIF":2.9,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph linear canonical transform based on CM-CC-CM decomposition

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-01-21 DOI: 10.1016/j.dsp.2025.105015

Na Li , Zhichao Zhang , Jie Han , Yunjie Chen , Chunzheng Cao

Graph linear canonical transform (GLCT) is an extension of graph Fourier transform (GFT) and graph fractional Fourier transform (GFrFT), offering more flexibility as an effective tool for graph signal processing. In this paper, we introduce GLCT based on chirp multiplication-chirp convolution-chirp multiplication decomposition (CM-CC-CM-GLCT), which is irrelevant to sampling periods and without oversampling operation. Accordingly, various properties of CM-CC-CM-GLCT are derived and discussed. Theoretical analysis and simulation results show that in comparison to GLCT based on the central discrete dilated Hermite function (CDDHFs-GLCT), CM-CC-CM-GLCT can achieve lower computational complexity, similar additivity, and better reversibility. Finally, the CM-CC-CM-GLCT method is applied to the field of data compression and its compression effect is compared with that of GFrFT and CDDHFs-GLCT to demonstrate its advantages in practical application.

引用次数: 0

Fault diagnosis of high-speed rolling bearings based on multi-feature fusion fuzzy c-means

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-01-20 DOI: 10.1016/j.dsp.2025.105011

Wenguang Luo, Junning Li, Maokang Song, Jinpeng Wen

The performance degradation of rolling bearings in the whole life cycle presents a nonlinear and complex process. Recently, many studies have shown that the degradation trend has multi-stage characteristics. It is difficult to distinguish the degradation features at different stages. To address this challenge, a novel fault diagnosis method of high-speed rolling bearings in the whole life cycle based on multi-feature fusion fuzzy c-means (MFF-FCM), wavelet thresholding denoising, improved grey wolf optimizer-variational mode decomposition (IGWO-VMD) and dual-channel convolutional neural network (DC-CNN) is proposed. Firstly, multi-feature information are adopted to characterize the nonlinear degradation trend, and MFF-FCM is used to divide the degradation stages. Subsequently, the fault feature is double extracted by the combination of VMD and DC-CNN. Finally, experimental results and engineering applications demonstrate that the average fault identification accuracy of this method can reach 98.275 %, which is crucial to fault diagnosis of high-speed rolling bearings in the whole life cycle.

{"title":"Fault diagnosis of high-speed rolling bearings based on multi-feature fusion fuzzy c-means","authors":"Wenguang Luo, Junning Li, Maokang Song, Jinpeng Wen","doi":"10.1016/j.dsp.2025.105011","DOIUrl":"10.1016/j.dsp.2025.105011","url":null,"abstract":"<div><div>The performance degradation of rolling bearings in the whole life cycle presents a nonlinear and complex process. Recently, many studies have shown that the degradation trend has multi-stage characteristics. It is difficult to distinguish the degradation features at different stages. To address this challenge, a novel fault diagnosis method of high-speed rolling bearings in the whole life cycle based on multi-feature fusion fuzzy c-means (MFF-FCM), wavelet thresholding denoising, improved grey wolf optimizer-variational mode decomposition (IGWO-VMD) and dual-channel convolutional neural network (DC-CNN) is proposed. Firstly, multi-feature information are adopted to characterize the nonlinear degradation trend, and MFF-FCM is used to divide the degradation stages. Subsequently, the fault feature is double extracted by the combination of VMD and DC-CNN. Finally, experimental results and engineering applications demonstrate that the average fault identification accuracy of this method can reach 98.275 %, which is crucial to fault diagnosis of high-speed rolling bearings in the whole life cycle.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 105011"},"PeriodicalIF":2.9,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Anti-main lobe suppression jamming using signal separation network

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-01-20 DOI: 10.1016/j.dsp.2025.105017

Yunyun Meng, Lei Yu, Yinsheng Wei

The main lobe suppression jamming seriously damages radar detection by covering the target echo in multiple domains. When the target and the jammer are in the same direction, the traditional blind source separation-based methods are ineffective. To effectively suppress jamming and achieve target detection, this paper proposes an end-to-end framework implemented by a complex-valued dual-path convolutional shrinkage time-domain signal separation network (CVDPCS-TssNet) to automatically separate mixed signals and recover target signals for jamming suppression. The jamming suppression framework is designed with an encoder-separation-decoder structure. Firstly, the encoder converts the received mixed signal into a representation in a separable feature domain. Then, the separation module learns the optimal separation weights in the feature domain to extract the jamming and target signal representations. Finally, the weighted signal representations are recovered into independent jamming signals and target signals by the decoder. Utilizing the advantage of the integrated multiple network components in signal sequence modeling and robust weak information representation, the CVDPCS-TssNet uses only the single-channel observed signal to recover the time-domain target signal. It is applicable to the scenario where the target and jammer are in the same direction. Experimental results on noise modulation jamming verify that the proposed method is superior in signal separation, jamming suppression, target detection performance and robust to varying signal-to-noise ratios and jamming-to-signal ratios.

{"title":"Anti-main lobe suppression jamming using signal separation network","authors":"Yunyun Meng, Lei Yu, Yinsheng Wei","doi":"10.1016/j.dsp.2025.105017","DOIUrl":"10.1016/j.dsp.2025.105017","url":null,"abstract":"<div><div>The main lobe suppression jamming seriously damages radar detection by covering the target echo in multiple domains. When the target and the jammer are in the same direction, the traditional blind source separation-based methods are ineffective. To effectively suppress jamming and achieve target detection, this paper proposes an end-to-end framework implemented by a complex-valued dual-path convolutional shrinkage time-domain signal separation network (CVDPCS-TssNet) to automatically separate mixed signals and recover target signals for jamming suppression. The jamming suppression framework is designed with an encoder-separation-decoder structure. Firstly, the encoder converts the received mixed signal into a representation in a separable feature domain. Then, the separation module learns the optimal separation weights in the feature domain to extract the jamming and target signal representations. Finally, the weighted signal representations are recovered into independent jamming signals and target signals by the decoder. Utilizing the advantage of the integrated multiple network components in signal sequence modeling and robust weak information representation, the CVDPCS-TssNet uses only the single-channel observed signal to recover the time-domain target signal. It is applicable to the scenario where the target and jammer are in the same direction. Experimental results on noise modulation jamming verify that the proposed method is superior in signal separation, jamming suppression, target detection performance and robust to varying signal-to-noise ratios and jamming-to-signal ratios.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 105017"},"PeriodicalIF":2.9,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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