Pub Date : 2026-04-15Epub Date: 2026-01-29DOI: 10.1016/j.dsp.2026.105967
Changqing Song , Dian Xiao , Wanbing Hao , Wanzhi Ma , Hongzhi Zhao , Shihai Shao
Driven by dual demands of spectrum-intensive military electronic warfare systems and high-spectral-efficiency civilian communications, simultaneous transmit-receive (STAR) array technology has gained significant attention due to its potential for efficient spectrum reuse. However, strong self-interference (SI) between transmit and receive channels degrades the receiver sensitivity, posing a critical technical barrier to its practical implementation. This study systematically reviews the research progress in STAR array SI cancellation technologies, covering five key aspects: SI coupling channels, spatial-domain cancellation, analog-domain cancellation, digital-domain cancellation, and experimental verification. Current state-of-the-art systems demonstrate up to 137.3 dB of isolation for 256 × 256 STAR arrays and 140.5 dB for 4 × 4 arrays, approaching engineering feasibility. Nevertheless, the large-scale deployment of multi-antenna arrays in civil and military applications will expose STAR arrays to more severe challenges from strong near-field SI. Future research should focus on clarifying near-field coupling mechanisms, optimizing spatial degrees of freedom, reducing the complexity of SI reconstruction, and refining compensation strategies for non-ideal factors to advance the deployment of STAR technology.
{"title":"A Review of self-interference cancellation technologies for simultaneous transmit-receive arrays","authors":"Changqing Song , Dian Xiao , Wanbing Hao , Wanzhi Ma , Hongzhi Zhao , Shihai Shao","doi":"10.1016/j.dsp.2026.105967","DOIUrl":"10.1016/j.dsp.2026.105967","url":null,"abstract":"<div><div>Driven by dual demands of spectrum-intensive military electronic warfare systems and high-spectral-efficiency civilian communications, simultaneous transmit-receive (STAR) array technology has gained significant attention due to its potential for efficient spectrum reuse. However, strong self-interference (SI) between transmit and receive channels degrades the receiver sensitivity, posing a critical technical barrier to its practical implementation. This study systematically reviews the research progress in STAR array SI cancellation technologies, covering five key aspects: SI coupling channels, spatial-domain cancellation, analog-domain cancellation, digital-domain cancellation, and experimental verification. Current state-of-the-art systems demonstrate up to 137.3 dB of isolation for 256 × 256 STAR arrays and 140.5 dB for 4 × 4 arrays, approaching engineering feasibility. Nevertheless, the large-scale deployment of multi-antenna arrays in civil and military applications will expose STAR arrays to more severe challenges from strong near-field SI. Future research should focus on clarifying near-field coupling mechanisms, optimizing spatial degrees of freedom, reducing the complexity of SI reconstruction, and refining compensation strategies for non-ideal factors to advance the deployment of STAR technology.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105967"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174907","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}
Pub Date : 2026-04-15Epub Date: 2026-01-27DOI: 10.1016/j.dsp.2026.105952
Mingquan Wang , Huiying Xu , Yiming Sun , Hongbo Li , Zeyu Wang , Yi Li , Ruidong Wang , Xinzhong Zhu
Object detection in UAV aerial images holds significant application value in traffic monitoring, precision agriculture, and other fields. However, this task faces numerous challenges, including significant variations in object sizes, complex background interference, high object density, and class imbalance. Additionally, processing high-resolution aerial images involves disturbances such as uneven lighting and weather variations. To address these challenges, we propose an EMFNet model. This model effectively solves the problems in object detection in drone aerial images by enhancing the response to object areas under different lighting and weather conditions, suppressing interference from complex backgrounds, and improving adaptability to changes in image object size. Specifically, firstly, the lightweight vision transformer architecture RepViT is innovatively used as the backbone of EMFNet, combined with Dual Cross-Stage Partial Attention (DCPA) to optimize multi-scale feature fusion and background suppression, thereby enhancing small object feature extraction under varying lighting and weather conditions. Second, we propose the Context Guided Downsample Block (CGDB) to improve the downsampling process and mitigate feature information loss. Finally, the DyHead detection head utilizing the three-level attention mechanism receives three appropriately located prediction heads for classification and localization, thus improving the detection accuracy of dense and rare objects. Experiments on the VisDrone and UAVDT datasets demonstrate that EMFNet, with 6.76M parameters, achieves AP improvements of 7.5% and 15.2% over the baseline models, respectively.
{"title":"EMFNet: An efficient multi-scale fusion network for UAV small object detection","authors":"Mingquan Wang , Huiying Xu , Yiming Sun , Hongbo Li , Zeyu Wang , Yi Li , Ruidong Wang , Xinzhong Zhu","doi":"10.1016/j.dsp.2026.105952","DOIUrl":"10.1016/j.dsp.2026.105952","url":null,"abstract":"<div><div>Object detection in UAV aerial images holds significant application value in traffic monitoring, precision agriculture, and other fields. However, this task faces numerous challenges, including significant variations in object sizes, complex background interference, high object density, and class imbalance. Additionally, processing high-resolution aerial images involves disturbances such as uneven lighting and weather variations. To address these challenges, we propose an EMFNet model. This model effectively solves the problems in object detection in drone aerial images by enhancing the response to object areas under different lighting and weather conditions, suppressing interference from complex backgrounds, and improving adaptability to changes in image object size. Specifically, firstly, the lightweight vision transformer architecture RepViT is innovatively used as the backbone of EMFNet, combined with <strong>D</strong>ual <strong>C</strong>ross-Stage <strong>P</strong>artial <strong>A</strong>ttention (<strong>DCPA</strong>) to optimize multi-scale feature fusion and background suppression, thereby enhancing small object feature extraction under varying lighting and weather conditions. Second, we propose the <strong>C</strong>ontext <strong>G</strong>uided <strong>D</strong>ownsample <strong>B</strong>lock (<strong>CGDB</strong>) to improve the downsampling process and mitigate feature information loss. Finally, the DyHead detection head utilizing the three-level attention mechanism receives three appropriately located prediction heads for classification and localization, thus improving the detection accuracy of dense and rare objects. Experiments on the VisDrone and UAVDT datasets demonstrate that EMFNet, with 6.76M parameters, achieves <em>AP</em> improvements of 7.5% and 15.2% over the baseline models, respectively.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105952"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070886","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}
Pub Date : 2026-04-15Epub Date: 2026-01-29DOI: 10.1016/j.dsp.2026.105962
Xiang Chen, Shuzhen Zhang, Hailong Song, Qi Yan
Recently, deformable convolutions based on convolutional neural networks have been widely used in hyperspectral image (HSI) classification due to their flexible geometric adaptability and superior local feature extraction capabilities. However, they still face significant challenges in establishing long-range dependencies and capturing global contextual information among pixel sequences. To address these challenges, a novel deformable convolution and Transformer hybrid network (DTHNet) is proposed for HSI classification. Specifically, PCA is firstly employed to reduce the dimensionality of the original HSI and a group depth joint convolution block (GDJCB) is utilized to capture the spectral-spatial features of the reduced HSI patches, avoiding the neglect of certain spectral bands. Secondly, a parallel architecture composed of a designed deformable convolution and a Transformer is utilized to jointly extract local-global spectral-spatial features and long-range dependencies in HSI. In the deformable convolution branch, a simple parameter-free attention (SimAM) enhanced spectral-spatial convolution block (SSCB) is designed to effectively prevent the loss of key information and the generation of redundant features during the convolution. In the Transformer branch, the deep integration of convolutional operation and self-attention mechanism further promotes more effective extraction of HSI features. Finally, fusion features from the two branches to obtain the more accurate HSI classification. Experimental results on three widely used HSI datasets demonstrate that the proposed DTHNet outperforms several state-of-the-art HSI classification networks.
{"title":"Deformable convolution and transformer hybrid network for hyperspectral image classification","authors":"Xiang Chen, Shuzhen Zhang, Hailong Song, Qi Yan","doi":"10.1016/j.dsp.2026.105962","DOIUrl":"10.1016/j.dsp.2026.105962","url":null,"abstract":"<div><div>Recently, deformable convolutions based on convolutional neural networks have been widely used in hyperspectral image (HSI) classification due to their flexible geometric adaptability and superior local feature extraction capabilities. However, they still face significant challenges in establishing long-range dependencies and capturing global contextual information among pixel sequences. To address these challenges, a novel deformable convolution and Transformer hybrid network (DTHNet) is proposed for HSI classification. Specifically, PCA is firstly employed to reduce the dimensionality of the original HSI and a group depth joint convolution block (GDJCB) is utilized to capture the spectral-spatial features of the reduced HSI patches, avoiding the neglect of certain spectral bands. Secondly, a parallel architecture composed of a designed deformable convolution and a Transformer is utilized to jointly extract local-global spectral-spatial features and long-range dependencies in HSI. In the deformable convolution branch, a simple parameter-free attention (SimAM) enhanced spectral-spatial convolution block (SSCB) is designed to effectively prevent the loss of key information and the generation of redundant features during the convolution. In the Transformer branch, the deep integration of convolutional operation and self-attention mechanism further promotes more effective extraction of HSI features. Finally, fusion features from the two branches to obtain the more accurate HSI classification. Experimental results on three widely used HSI datasets demonstrate that the proposed DTHNet outperforms several state-of-the-art HSI classification networks.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105962"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081704","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}
Pub Date : 2026-04-15Epub Date: 2026-01-23DOI: 10.1016/j.dsp.2026.105938
Runtian Zheng, Congpeng Zhang, Ying Liu
Accurately detecting tiny targets in microscopic images is critical for tuberculosis screening yet remains difficult due to large shape variation, dense instances with weak semantics, and cluttered backgrounds. We curate a Mycobacterium tuberculosis dataset of 5,842 microscopic images and present EFDNet, an Enhanced Feature Fusion and Detail-Preserving detector. EFDNet combines an Adaptive Feature Enhancement module that dynamically shifts convolutional sampling to capture irregular, fine-grained patterns, a Cross-Stage Enhanced Feature Pyramid Network that fuses semantic and localization cues across scales to withstand crowding and background clutter, and a lightweight shared Detail-Enhanced detection head that preserves high-frequency structure through differential convolutions and shared parameters, together with a Normalized Wasserstein Distance loss that reduces localization sensitivity for small boxes. On our dataset, the Tuberculosis-Phonecamera dataset, and the cross-domain BBBC041 blood-cell benchmark, EFDNet achieves AP50 of 81.9%, 87.6%, and 95.2%, outperforming a strong baseline by , , and points, respectively, while maintaining low computational cost. These results indicate robust small-object detection under varied microscopy conditions and support the practical utility of EFDNet for automated screening.
{"title":"Enhanced feature fusion and detail-Preserving network for small object detection in medical microscopic images","authors":"Runtian Zheng, Congpeng Zhang, Ying Liu","doi":"10.1016/j.dsp.2026.105938","DOIUrl":"10.1016/j.dsp.2026.105938","url":null,"abstract":"<div><div>Accurately detecting tiny targets in microscopic images is critical for tuberculosis screening yet remains difficult due to large shape variation, dense instances with weak semantics, and cluttered backgrounds. We curate a Mycobacterium tuberculosis dataset of 5,842 microscopic images and present EFDNet, an Enhanced Feature Fusion and Detail-Preserving detector. EFDNet combines an Adaptive Feature Enhancement module that dynamically shifts convolutional sampling to capture irregular, fine-grained patterns, a Cross-Stage Enhanced Feature Pyramid Network that fuses semantic and localization cues across scales to withstand crowding and background clutter, and a lightweight shared Detail-Enhanced detection head that preserves high-frequency structure through differential convolutions and shared parameters, together with a Normalized Wasserstein Distance loss that reduces localization sensitivity for small boxes. On our dataset, the Tuberculosis-Phonecamera dataset, and the cross-domain BBBC041 blood-cell benchmark, EFDNet achieves <em>AP</em><sub>50</sub> of 81.9%, 87.6%, and 95.2%, outperforming a strong baseline by <span><math><mrow><mo>+</mo><mn>5.7</mn></mrow></math></span>, <span><math><mrow><mo>+</mo><mn>3.2</mn></mrow></math></span>, and <span><math><mrow><mo>+</mo><mn>3.9</mn></mrow></math></span> points, respectively, while maintaining low computational cost. These results indicate robust small-object detection under varied microscopy conditions and support the practical utility of EFDNet for automated screening.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105938"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081705","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}
Pub Date : 2026-04-15Epub Date: 2026-02-05DOI: 10.1016/j.dsp.2026.105981
Wei Li , Yanxue Zhou , Lvchen Cao , Wenjiao Li , Xiuli Chai
In visual security image encryption algorithms, simultaneously ensuring security and high-quality restored images remains challenging. In this paper, we propose a method that incorporates deep learning-based super-resolution reconstruction as a key post-processing step after the encryption-decryption steps. This approach aims to achieve high-quality reconstructed images while maintaining decryption security. Specifically, a 1D Chebyshev-hyperbolic composite chaotic map (1D-CHCCM) is firstly proposed. Its superior chaotic behavior and stability are validated through multidimensional analysis, including Lyapunov exponent, sample entropy, and permutation entropy. To address the traditional channel independent processing, the symmetric cross-channel circular scrambling (SC3S) and odd-even alternating diffusion (OEAD) are proposed. These mechanisms treat color images as unified entities to enhance resistance to attacks. Furthermore, for visual concealment during transmission, a texture-based adaptive data hiding (ATADH) scheme is utilized to guarantee steganographic images (STIs) are visually indistinguishable. After decryption, the decrypted image is fed into a Transformer-based super-resolution reconstruction network to obtain the final high-quality image. Quantitative analysis reveals that the proposed algorithm achieves a correlation coefficient below 0.003, an information entropy of 7.9973, and NPCR/UACI scores of 99.61% and 33.42%. In terms of visual quality, the STIs maintain excellent imperceptibility with a PSNR of 48.8 dB, and these reconstructed images have reached a PSNR of 41 dB. These results confirm that the goal of balancing security and high-quality image restoration is achieved.
{"title":"A visual security image encryption algorithm based on 1D-CHCCM and super-resolution reconstruction","authors":"Wei Li , Yanxue Zhou , Lvchen Cao , Wenjiao Li , Xiuli Chai","doi":"10.1016/j.dsp.2026.105981","DOIUrl":"10.1016/j.dsp.2026.105981","url":null,"abstract":"<div><div>In visual security image encryption algorithms, simultaneously ensuring security and high-quality restored images remains challenging. In this paper, we propose a method that incorporates deep learning-based super-resolution reconstruction as a key post-processing step after the encryption-decryption steps. This approach aims to achieve high-quality reconstructed images while maintaining decryption security. Specifically, a 1D Chebyshev-hyperbolic composite chaotic map (1D-CHCCM) is firstly proposed. Its superior chaotic behavior and stability are validated through multidimensional analysis, including Lyapunov exponent, sample entropy, and permutation entropy. To address the traditional channel independent processing, the symmetric cross-channel circular scrambling (SC<sup>3</sup>S) and odd-even alternating diffusion (OEAD) are proposed. These mechanisms treat color images as unified entities to enhance resistance to attacks. Furthermore, for visual concealment during transmission, a texture-based adaptive data hiding (ATADH) scheme is utilized to guarantee steganographic images (STIs) are visually indistinguishable. After decryption, the decrypted image is fed into a Transformer-based super-resolution reconstruction network to obtain the final high-quality image. Quantitative analysis reveals that the proposed algorithm achieves a correlation coefficient below 0.003, an information entropy of 7.9973, and NPCR/UACI scores of 99.61% and 33.42%. In terms of visual quality, the STIs maintain excellent imperceptibility with a PSNR of 48.8 dB, and these reconstructed images have reached a PSNR of 41 dB. These results confirm that the goal of balancing security and high-quality image restoration is achieved.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105981"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174816","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}
Pub Date : 2026-04-15Epub Date: 2026-02-04DOI: 10.1016/j.dsp.2026.105960
Olivia Zacharia, Vani Devi M․
This paper presents an orthogonal time-frequency space (OTFS)-based integrated sensing and communication (ISAC) transceiver architecture designed for vehicular platforms, enabling simultaneous environment sensing and data exchange with roadside units. A novel multiple-input multiple-output (MIMO) channel matrix model is introduced to account for fractional delays and Doppler shifts misaligned with the discrete delay-Doppler resolution of the OTFS grid. We derive a sparse time-domain input-output relationship for the MIMO-OTFS system and propose a two-stage delay-Doppler-angular fractional refinement (DDAFR) algorithm for joint estimation of delay, Doppler, and angle parameters. Compared to orthogonal matching pursuit (OMP), the proposed method offers lower complexity by avoiding the use of large dictionary matrices. To further mitigate the processing overhead, we propose a modified DDAFR (MDDAFR) algorithm that first determines the angle of arrival (AoA), followed by the remaining parameters. Simulation results confirm that the proposed ISAC algorithms achieve robust estimation performance while maintaining computational efficiency.
{"title":"Low Complexity estimation of fractional delay-Doppler-Angle parameters in MIMO-OTFS ISAC system","authors":"Olivia Zacharia, Vani Devi M․","doi":"10.1016/j.dsp.2026.105960","DOIUrl":"10.1016/j.dsp.2026.105960","url":null,"abstract":"<div><div>This paper presents an orthogonal time-frequency space (OTFS)-based integrated sensing and communication (ISAC) transceiver architecture designed for vehicular platforms, enabling simultaneous environment sensing and data exchange with roadside units. A novel multiple-input multiple-output (MIMO) channel matrix model is introduced to account for fractional delays and Doppler shifts misaligned with the discrete delay-Doppler resolution of the OTFS grid. We derive a sparse time-domain input-output relationship for the MIMO-OTFS system and propose a two-stage delay-Doppler-angular fractional refinement (DDAFR) algorithm for joint estimation of delay, Doppler, and angle parameters. Compared to orthogonal matching pursuit (OMP), the proposed method offers lower complexity by avoiding the use of large dictionary matrices. To further mitigate the processing overhead, we propose a modified DDAFR (MDDAFR) algorithm that first determines the angle of arrival (AoA), followed by the remaining parameters. Simulation results confirm that the proposed ISAC algorithms achieve robust estimation performance while maintaining computational efficiency.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105960"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174820","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}
In complex electromagnetic environments, radar pulse signals are strongly affected by noise, and limitations of reconnaissance receivers enlarge measurement errors, causing severe pulse missing and the inclusion of numerous spurious pulses. Consequently, pulse sorting faces two key difficulties: mining pulse association relations under missing information, and maintaining inter-class separability under serious parameter feature overlap. We propose a semi-supervised radar signal sorting method based on multiview subspace representation and graph learning (MvSR-GCN-RSS). First, encoders map multiple views into the latent space, where the view-specific and universal self-representation matrices are solved, and pulse sequence adjacency relations are constructed from intrapulse and interpulse information. Then, multiview information complementarity is achieved through a consistency loss and a diversity loss. In contrast to the two-stage process of first graph construction and then spectral clustering, we couple adjacency matrix solving with a graph convolutional network (GCN) in a single end-to-end framework, jointly optimizing it with the parameters of the multiview encoders and decoders to improve sorting efficiency. Finally, we design a multiview joint loss that simultaneously optimizes view reconstruction, GCN-based classification, self-representation solving, and cross-view complementarity for radar signal sorting. Simulation results show that the sorting accuracy reaches 99.99% in ideal scenarios; under scenarios with large measurement errors, pulse missing, and numerous spurious pulses, the proposed method performs far better than the comparison algorithms.
{"title":"Semi-supervised radar signal sorting with multiview subspace representations and graph learning","authors":"Shuai Huang , Qiang Guo , Yuhang Tian , Hao Feng , Sergey Shulga","doi":"10.1016/j.dsp.2026.105963","DOIUrl":"10.1016/j.dsp.2026.105963","url":null,"abstract":"<div><div>In complex electromagnetic environments, radar pulse signals are strongly affected by noise, and limitations of reconnaissance receivers enlarge measurement errors, causing severe pulse missing and the inclusion of numerous spurious pulses. Consequently, pulse sorting faces two key difficulties: mining pulse association relations under missing information, and maintaining inter-class separability under serious parameter feature overlap. We propose a semi-supervised radar signal sorting method based on multiview subspace representation and graph learning (MvSR-GCN-RSS). First, encoders map multiple views into the latent space, where the view-specific and universal self-representation matrices are solved, and pulse sequence adjacency relations are constructed from intrapulse and interpulse information. Then, multiview information complementarity is achieved through a consistency loss and a diversity loss. In contrast to the two-stage process of first graph construction and then spectral clustering, we couple adjacency matrix solving with a graph convolutional network (GCN) in a single end-to-end framework, jointly optimizing it with the parameters of the multiview encoders and decoders to improve sorting efficiency. Finally, we design a multiview joint loss that simultaneously optimizes view reconstruction, GCN-based classification, self-representation solving, and cross-view complementarity for radar signal sorting. Simulation results show that the sorting accuracy reaches 99.99% in ideal scenarios; under scenarios with large measurement errors, pulse missing, and numerous spurious pulses, the proposed method performs far better than the comparison algorithms.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105963"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174904","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}
Pub Date : 2026-04-15Epub Date: 2026-01-29DOI: 10.1016/j.dsp.2026.105968
Yixuan Shen, Mei Da, Lin Jiang
To address the deficiencies of existing infrared image detection models in terms of detection accuracy, computational complexity, detection speed, as well as missed detections and false detections in complex backgrounds, this paper proposes a lightweight infrared small target detection algorithm: YOLO - MBL. Firstly, we design a Dynamic Convolution Multi - Path Fusion Module (DCMP) to replace the original C3k2 module to enhance the feature extraction capability of the network. Secondly, we design the SDI - BiFPN as a feature fusion module in the neck network to capture more comprehensive feature information, thereby effectively avoiding the loss of information during the transmission process. Furthermore, a Lightweight Shared Convolutional Detection Head (LSCD) is introduced to reduce the number of model parameters. Finally, the Wise - MPDIoU loss function is adopted to accelerate the model convergence process and enhance its detection accuracy. To validate the effectiveness of the YOLO - MBL algorithm, we conducted comparative experiments on the FLIR dataset and the HIT - UAV dataset. The experimental results demonstrate that the YOLO - MBL model achieves a 4.6% improvement in detection accuracy ([email protected]) on the FLIR dataset, with a parameter reduction of 0.2 M, and reaches an FPS of 81.1. On the HIT - UAV dataset, the model's detection accuracy ([email protected]) is enhanced by 3.7%, accompanied by a parameter reduction of 0.2 M, and the FPS attains 84.1. Compared with traditional algorithms and current mainstream one - stage detection algorithms, the YOLO - MBL algorithm demonstrates significant advantages in terms of detection accuracy. The code repository is available at: https://github.com/yixixi12/YOLO-MBL.git.
{"title":"YOLO-MBL: An infrared small target detection algorithm based on YOLOv11","authors":"Yixuan Shen, Mei Da, Lin Jiang","doi":"10.1016/j.dsp.2026.105968","DOIUrl":"10.1016/j.dsp.2026.105968","url":null,"abstract":"<div><div>To address the deficiencies of existing infrared image detection models in terms of detection accuracy, computational complexity, detection speed, as well as missed detections and false detections in complex backgrounds, this paper proposes a lightweight infrared small target detection algorithm: YOLO - MBL. Firstly, we design a Dynamic Convolution Multi - Path Fusion Module (DCMP) to replace the original C3k2 module to enhance the feature extraction capability of the network. Secondly, we design the SDI - BiFPN as a feature fusion module in the neck network to capture more comprehensive feature information, thereby effectively avoiding the loss of information during the transmission process. Furthermore, a Lightweight Shared Convolutional Detection Head (LSCD) is introduced to reduce the number of model parameters. Finally, the Wise - MPDIoU loss function is adopted to accelerate the model convergence process and enhance its detection accuracy. To validate the effectiveness of the YOLO - MBL algorithm, we conducted comparative experiments on the FLIR dataset and the HIT - UAV dataset. The experimental results demonstrate that the YOLO - MBL model achieves a 4.6% improvement in detection accuracy ([email protected]) on the FLIR dataset, with a parameter reduction of 0.2 M, and reaches an FPS of 81.1. On the HIT - UAV dataset, the model's detection accuracy ([email protected]) is enhanced by 3.7%, accompanied by a parameter reduction of 0.2 M, and the FPS attains 84.1. Compared with traditional algorithms and current mainstream one - stage detection algorithms, the YOLO - MBL algorithm demonstrates significant advantages in terms of detection accuracy. The code repository is available at: <span><span>https://github.com/yixixi12/YOLO-MBL.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105968"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174905","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}
Pub Date : 2026-04-15Epub Date: 2026-01-27DOI: 10.1016/j.dsp.2026.105961
Xuerong Cui , Kai Zheng , Juan Li , Lei Li , Bin Jiang
To support the detection and communication requirements of offshore devices operating under stringent resource constraints, it is essential to overcome the challenges posed by complex underwater acoustic channels and intense ocean noise. Consequently, designing a lightweight automatic modulation recognition (AMR) algorithm for integrated underwater acoustic detection and communication signals is particularly challenging. Despite recent advances, current AMR algorithms still exhibit limitations in computational speed and resource usage. Moreover, to date, no AMR method has been specifically designed for integrated acoustic detection and communication (IADC) signal frameworks. To address these issues, this paper proposes a Wavelet Complex Convolution Network (WCC-Net) that directly uses in-phase/quadrature (I/Q) signals as input. First, the in-phase and quadrature components of the signal are each fed into two independent wavelet convolution modules, which simultaneously enlarge the receptive field and suppress noise. Then, a complex convolution module preserves the phase coupling information while efficiently mixing the feature information. Finally, an efficient feature mixing module combines and refines the high-dimensional features to produce the classification result, reducing redundant information and enhancing feature interaction. Experimental results indicate that, at about 89% recognition accuracy, WCC-Net reduces the computational complexity by 84.76% and the number of parameters by 88.82%; under the same model complexity, WCC-Net accuracy is improved by at least 6.91%. Even under real-world ocean noise conditions, WCC-Net attains competitive recognition accuracy with minimal model complexity.
{"title":"WCC-Net : Lightweight automatic modulation recognition of integrated underwater acoustic signals","authors":"Xuerong Cui , Kai Zheng , Juan Li , Lei Li , Bin Jiang","doi":"10.1016/j.dsp.2026.105961","DOIUrl":"10.1016/j.dsp.2026.105961","url":null,"abstract":"<div><div>To support the detection and communication requirements of offshore devices operating under stringent resource constraints, it is essential to overcome the challenges posed by complex underwater acoustic channels and intense ocean noise. Consequently, designing a lightweight automatic modulation recognition (AMR) algorithm for integrated underwater acoustic detection and communication signals is particularly challenging. Despite recent advances, current AMR algorithms still exhibit limitations in computational speed and resource usage. Moreover, to date, no AMR method has been specifically designed for integrated acoustic detection and communication (IADC) signal frameworks. To address these issues, this paper proposes a Wavelet Complex Convolution Network (WCC-Net) that directly uses in-phase/quadrature (I/Q) signals as input. First, the in-phase and quadrature components of the signal are each fed into two independent wavelet convolution modules, which simultaneously enlarge the receptive field and suppress noise. Then, a complex convolution module preserves the phase coupling information while efficiently mixing the feature information. Finally, an efficient feature mixing module combines and refines the high-dimensional features to produce the classification result, reducing redundant information and enhancing feature interaction. Experimental results indicate that, at about 89% recognition accuracy, WCC-Net reduces the computational complexity by 84.76% and the number of parameters by 88.82%; under the same model complexity, WCC-Net accuracy is improved by at least 6.91%. Even under real-world ocean noise conditions, WCC-Net attains competitive recognition accuracy with minimal model complexity.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105961"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174909","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}
Pub Date : 2026-04-15Epub Date: 2026-01-21DOI: 10.1016/j.dsp.2026.105948
Zikai Chen , Degang Yang , Tingting Song , Yichen Ye , Yongli Liu , Xin Zhang
With the continuous development of deep learning technology, object detection tasks in remote sensing images have received increasing attention. However, due to the diversity of object scales and the complexity of background environments, current detectors often find it difficult to control computational costs while ensuring high performance. To address these challenges, we design a remote sensing image object detector called MA-YOLO, which integrates multi-scale features and attention mechanisms. We design the mixed receptive field attention convolution (MRFAConv) module to strengthen the backbone network, which is a non-parametric shared convolution that takes into account both spatial and channel attention. Moreover, a multi-scale receptive field downsampling module (MRFD) is proposed, which can extract rich feature information from different receptive fields while effectively reducing information loss. Ultimately, a lightweight multi-scale attention module (LMSA) is designed and integrated into the neck network to further optimize the feature fusion effect. Extensive experiments conducted on the DIOR and TGRS-HRRSD datasets reveal that MA-YOLO enhances the mAP by 2.1% and 5.3%, respectively, compared to the baseline model YOLOv8n, while slightly reducing computational overhead and decreasing the number of parameters by 6.7%. These experimental results fully demonstrate the remarkable effectiveness of our proposed method in enhancing the detection accuracy of remote sensing images. The code will be available at https://github.com/Zikai-Chen/MA-YOLO.
{"title":"MA-YOLO: Enhanced multi-scale attentional remote sensing detector","authors":"Zikai Chen , Degang Yang , Tingting Song , Yichen Ye , Yongli Liu , Xin Zhang","doi":"10.1016/j.dsp.2026.105948","DOIUrl":"10.1016/j.dsp.2026.105948","url":null,"abstract":"<div><div>With the continuous development of deep learning technology, object detection tasks in remote sensing images have received increasing attention. However, due to the diversity of object scales and the complexity of background environments, current detectors often find it difficult to control computational costs while ensuring high performance. To address these challenges, we design a remote sensing image object detector called MA-YOLO, which integrates multi-scale features and attention mechanisms. We design the mixed receptive field attention convolution (MRFAConv) module to strengthen the backbone network, which is a non-parametric shared convolution that takes into account both spatial and channel attention. Moreover, a multi-scale receptive field downsampling module (MRFD) is proposed, which can extract rich feature information from different receptive fields while effectively reducing information loss. Ultimately, a lightweight multi-scale attention module (LMSA) is designed and integrated into the neck network to further optimize the feature fusion effect. Extensive experiments conducted on the DIOR and TGRS-HRRSD datasets reveal that MA-YOLO enhances the mAP by 2.1% and 5.3%, respectively, compared to the baseline model YOLOv8n, while slightly reducing computational overhead and decreasing the number of parameters by 6.7%. These experimental results fully demonstrate the remarkable effectiveness of our proposed method in enhancing the detection accuracy of remote sensing images. The code will be available at <span><span>https://github.com/Zikai-Chen/MA-YOLO</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105948"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174910","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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