Pub Date : 2026-04-15Epub Date: 2026-01-22DOI: 10.1016/j.dsp.2026.105928
Huihui Ma , Haihong Tao , Yaxing Yue , Tiantian Zhong , Yunfei Fang , Le Wang
This study explores the multiparameter estimation challenge within bistatic frequency diverse array multiple-input-multiple-output (FDA-MIMO) radar system that employs arbitrarily configured electromagnetic vector sensor (EMVS) arrays. The signal reception model for the presented radar architecture is established. Building on this foundation, a subspace-based algorithm is proposed to achieve accurate estimation of spatial-polarization angles and ranges. First, rotation invariant structures in spatial domain are formed by constructing several virtual steering matrices, from which the normalized electromagnetic field vectors are derived. Then the two-dimensional direction-of-departure (2D-DOD) and two-dimensional direction-of-arrival (2D-DOA) estimates are computed through vector cross-product operation. Thereafter, polarization angles are determined using least squares (LS) approach. Finally, by compensating the steering matrix with the obtained 2D-DOD, the range estimation can be achieved. Furthermore, the developed framework is evaluated for its identifiability, flexibility, computational demands, and Cramér-Rao bound (CRB). It successfully estimates the targets’ spatial-polarization angles and ranges, while also achieving automatic parameters pairing. Simulation results demonstrate the validity of the developed approach.
{"title":"Multiparameter estimation for bistatic EMVS-FDA-MIMO radar with arbitrarily configured arrays","authors":"Huihui Ma , Haihong Tao , Yaxing Yue , Tiantian Zhong , Yunfei Fang , Le Wang","doi":"10.1016/j.dsp.2026.105928","DOIUrl":"10.1016/j.dsp.2026.105928","url":null,"abstract":"<div><div>This study explores the multiparameter estimation challenge within bistatic frequency diverse array multiple-input-multiple-output (FDA-MIMO) radar system that employs arbitrarily configured electromagnetic vector sensor (EMVS) arrays. The signal reception model for the presented radar architecture is established. Building on this foundation, a subspace-based algorithm is proposed to achieve accurate estimation of spatial-polarization angles and ranges. First, rotation invariant structures in spatial domain are formed by constructing several virtual steering matrices, from which the normalized electromagnetic field vectors are derived. Then the two-dimensional direction-of-departure (2D-DOD) and two-dimensional direction-of-arrival (2D-DOA) estimates are computed through vector cross-product operation. Thereafter, polarization angles are determined using least squares (LS) approach. Finally, by compensating the steering matrix with the obtained 2D-DOD, the range estimation can be achieved. Furthermore, the developed framework is evaluated for its identifiability, flexibility, computational demands, and Cramér-Rao bound (CRB). It successfully estimates the targets’ spatial-polarization angles and ranges, while also achieving automatic parameters pairing. Simulation results demonstrate the validity of the developed approach.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105928"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174903","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: 2026-02-02DOI: 10.1016/j.dsp.2026.105951
Zewen Han , Qiong Huang , Liantao Lan
Hyperspectral image (HSI) classification is a fundamental task in remote sensing. The main challenge lies in effectively capturing both spatial and spectral features. Recently, the Mamba model has received increasing attention for its linear capability in long-range modeling. However, when applied to HSI, it still suffers from the loss of spatial structural information and the neglect of spectral locality. In addition, compared with Transformers, Mamba remains limited in capturing global contextual dependencies. To address these challenges, we propose S2MFormer, a novel Mamba-Transformer hybrid network. It adopts a dual-branch architecture to comprehensively capture both spatial and spectral features from HSI. In the spatial branch, a spatial snake-like scanning strategy is designed to preserve locality during data transformation. To compensate for Mamba’s limitations in global feature capture, we introduce a spatial intra-scale Transformer module, which uses a multi-head attention mechanism to enhance the extraction of global spatial information. For the spectral branch, we employ a spectral grouping strategy for efficient local modeling. This is combined with a spectral intra-scale Transformer to capture multi-dimensional global spectral context. Finally, a spatial-spectral fusion module precisely fuses the spatial and spectral features using learnable weights. Extensive experiments on several public datasets demonstrate that S2MFormer significantly outperforms existing methods in classification accuracy, thus validating the superiority of our proposed approach. Codes are available at https://github.com/hzw123456663592/S2MFormer/tree/master.
{"title":"S2MFormer: Spatial-spectral Mamba-transformer complementary network for hyperspectral image classification","authors":"Zewen Han , Qiong Huang , Liantao Lan","doi":"10.1016/j.dsp.2026.105951","DOIUrl":"10.1016/j.dsp.2026.105951","url":null,"abstract":"<div><div>Hyperspectral image (HSI) classification is a fundamental task in remote sensing. The main challenge lies in effectively capturing both spatial and spectral features. Recently, the Mamba model has received increasing attention for its linear capability in long-range modeling. However, when applied to HSI, it still suffers from the loss of spatial structural information and the neglect of spectral locality. In addition, compared with Transformers, Mamba remains limited in capturing global contextual dependencies. To address these challenges, we propose S<sup>2</sup>MFormer, a novel Mamba-Transformer hybrid network. It adopts a dual-branch architecture to comprehensively capture both spatial and spectral features from HSI. In the spatial branch, a spatial snake-like scanning strategy is designed to preserve locality during data transformation. To compensate for Mamba’s limitations in global feature capture, we introduce a spatial intra-scale Transformer module, which uses a multi-head attention mechanism to enhance the extraction of global spatial information. For the spectral branch, we employ a spectral grouping strategy for efficient local modeling. This is combined with a spectral intra-scale Transformer to capture multi-dimensional global spectral context. Finally, a spatial-spectral fusion module precisely fuses the spatial and spectral features using learnable weights. Extensive experiments on several public datasets demonstrate that S<sup>2</sup>MFormer significantly outperforms existing methods in classification accuracy, thus validating the superiority of our proposed approach. Codes are available at <span><span>https://github.com/hzw123456663592/S2MFormer/tree/master</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105951"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174817","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: 2026-01-30DOI: 10.1016/j.dsp.2026.105954
Zhimin Lu , Qing Zhang , Boheng Tian , Fuhua Ge , Chenxi Mo , Rui Guo , Xianbin Duan , Chunming Guo , Pengfei Yu
Multichannel fluorescence imaging plays a pivotal role in cell type identification and pathological diagnosis. However, manual analysis of fluorescence images is prone to misdiagnoses and missed diagnoses. Although AI algorithms hold promise, current methods struggle to extract discriminative features, thereby compromising the accuracy of pathological analysis. This study proposes ASCF-RTDETR, a novel model for precisely detecting epithelial cells in multichannel fluorescence images. ASCF-RTDETR incorporates an Adaptive Multi-Scale Collaborative Feature Fusion (AMFF) module, enabling comprehensive feature interaction through horizontal and vertical dual-path parallel propagation. This is complemented by a High-Efficiency Feature Upsampling Convolution (HFUC) and Multi-Scale Convolution Block (MSCB), enhancing feature representation. Furthermore, a Dynamic Histogram Attention-based Intra-scale Feature Interaction (DHIFI) module is introduced, leveraging bin-wise and frequency-wise dual-path reconstruction to enhance cell boundary features. Concurrently, a lightweight Dual Convolution (DualConv) structure is integrated to reduce computational complexity and provide implicit regularization against imaging noise. Experiments on a self-constructed multichannel fluorescence-labeled epithelial cell dataset demonstrate ASCF-RTDETR’s superior detection performance, achieving a 93.5% mAP50 and 90.7% F1-score, with nearly 50% reduced computational cost compared to baseline models. The model also exhibits strong generalization across multiple public datasets, offering a reliable solution for automated epithelial cell detection and analysis.
{"title":"ASCF-RTDETR: Adaptive scale collaborative feature learning for epithelial cell detection in multichannel fluorescence images","authors":"Zhimin Lu , Qing Zhang , Boheng Tian , Fuhua Ge , Chenxi Mo , Rui Guo , Xianbin Duan , Chunming Guo , Pengfei Yu","doi":"10.1016/j.dsp.2026.105954","DOIUrl":"10.1016/j.dsp.2026.105954","url":null,"abstract":"<div><div>Multichannel fluorescence imaging plays a pivotal role in cell type identification and pathological diagnosis. However, manual analysis of fluorescence images is prone to misdiagnoses and missed diagnoses. Although AI algorithms hold promise, current methods struggle to extract discriminative features, thereby compromising the accuracy of pathological analysis. This study proposes ASCF-RTDETR, a novel model for precisely detecting epithelial cells in multichannel fluorescence images. ASCF-RTDETR incorporates an Adaptive Multi-Scale Collaborative Feature Fusion (AMFF) module, enabling comprehensive feature interaction through horizontal and vertical dual-path parallel propagation. This is complemented by a High-Efficiency Feature Upsampling Convolution (HFUC) and Multi-Scale Convolution Block (MSCB), enhancing feature representation. Furthermore, a Dynamic Histogram Attention-based Intra-scale Feature Interaction (DHIFI) module is introduced, leveraging bin-wise and frequency-wise dual-path reconstruction to enhance cell boundary features. Concurrently, a lightweight Dual Convolution (DualConv) structure is integrated to reduce computational complexity and provide implicit regularization against imaging noise. Experiments on a self-constructed multichannel fluorescence-labeled epithelial cell dataset demonstrate ASCF-RTDETR’s superior detection performance, achieving a 93.5% <em>mAP</em><sub>50</sub> and 90.7% <em>F1</em>-score, with nearly 50% reduced computational cost compared to baseline models. The model also exhibits strong generalization across multiple public datasets, offering a reliable solution for automated epithelial cell detection and analysis.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105954"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174902","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 : 2026-04-01Epub Date: 2026-01-19DOI: 10.1016/j.dsp.2026.105945
Gaoming Yang , Biaohu Sun , Xiujun Wang
The accelerated progression of deepfake technologies has triggered a serious trust crisis and motivated numerous scholars to pursue effective methods for detecting such forgeries. However, current detection methods heavily rely on limited forgery cues and irrelevant information to boost intra-dataset performance, and they struggle with generalization and robustness in real-world scenarios. To tackle these problems, we design a Multi-Scale Feature Pyramid Network (MS-FPN) that focuses on forgery regions, and an altered-trace enhancement strategy to reveal more tampering artifacts. Specifically, the MS-FPN performs forgery-region segmentation during feature extraction, which counteracts the detector’s reliance on forgery-irrelevant information and allows it to concentrate on more altered areas. Furthermore, a plug-and-play Cross-Feature Spatial Attention (CFSA) module is proposed to strengthen the constraints on high-level features. In addition, we develop the falsified images re-mixing method to highlight more generalized artifacts by blending two augmented forgery images, while a Multi-level Feature Fusion (MLFF) module is utilized to integrate multi-scale features, enabling the network to capture fine-grained local features. Extensive experiments on multiple public benchmarks demonstrate that the proposed method achieves superior cross-dataset and cross-manipulation generalization, achieving AUC scores of 93.22% on CDF2, 96.88% on UADFV, and 92.67% on DFD. Visualization results further confirm that our approach produces interpretable and reliable evidence for face forgery forensics. The code is available at https://github.com/Sun-researcher/SD-Net-main
{"title":"Exploring feature pyramid networks and feature fusion for generalized Deepfake detection","authors":"Gaoming Yang , Biaohu Sun , Xiujun Wang","doi":"10.1016/j.dsp.2026.105945","DOIUrl":"10.1016/j.dsp.2026.105945","url":null,"abstract":"<div><div>The accelerated progression of deepfake technologies has triggered a serious trust crisis and motivated numerous scholars to pursue effective methods for detecting such forgeries. However, current detection methods heavily rely on limited forgery cues and irrelevant information to boost intra-dataset performance, and they struggle with generalization and robustness in real-world scenarios. To tackle these problems, we design a Multi-Scale Feature Pyramid Network (MS-FPN) that focuses on forgery regions, and an altered-trace enhancement strategy to reveal more tampering artifacts. Specifically, the MS-FPN performs forgery-region segmentation during feature extraction, which counteracts the detector’s reliance on forgery-irrelevant information and allows it to concentrate on more altered areas. Furthermore, a plug-and-play Cross-Feature Spatial Attention (CFSA) module is proposed to strengthen the constraints on high-level features. In addition, we develop the falsified images re-mixing method to highlight more generalized artifacts by blending two augmented forgery images, while a Multi-level Feature Fusion (MLFF) module is utilized to integrate multi-scale features, enabling the network to capture fine-grained local features. Extensive experiments on multiple public benchmarks demonstrate that the proposed method achieves superior cross-dataset and cross-manipulation generalization, achieving AUC scores of 93.22% on CDF2, 96.88% on UADFV, and 92.67% on DFD. Visualization results further confirm that our approach produces interpretable and reliable evidence for face forgery forensics. The code is available at <span><span>https://github.com/Sun-researcher/SD-Net-main</span><svg><path></path></svg></span></div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"173 ","pages":"Article 105945"},"PeriodicalIF":3.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079706","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 : 2026-04-01Epub Date: 2026-01-19DOI: 10.1016/j.dsp.2026.105941
Omer Kocak , Uğur Erkan , Ismail Babaoglu
Chaos-based image encryption methods strongly depend on the complexity and dynamic behavior of chaotic maps to achieve effective permutation and diffusion. In this study, a novel two-dimensional Euler Pi Crossed Sine (2D-EPICS) chaotic map is introduced, which exhibits hyperchaotic dynamics, wide chaotic ranges, and high sensitivity to initial conditions. The chaotic properties of the proposed map are rigorously analyzed using bifurcation diagrams, phase trajectories, Lyapunov exponents, and multiple entropy measures, including sample entropy, permutation entropy, Kolmogorov entropy, and C0 complexity, confirming its strong nonlinear behavior and unpredictability. Building upon this chaotic foundation, the Bit-Level Quad-Block Shuffling and Sequential Summing Dispersing Image Encryption (BQSSSD-IE) scheme is then developed. The encryption process consists of a bit-level permutation stage based on quadruple pixel blocks, followed by a bidirectional diffusion stage achieved through cumulative row-wise and column-wise summations, both driven by sequences generated from the 2D-EPICS map. Extensive security analyses and comparative evaluations demonstrate that the proposed method provides high entropy, low pixel correlation, strong resistance against statistical, differential, noise, and cropping attacks, and competitive computational efficiency. The enhanced dynamic behavior of the 2D-EPICS map significantly strengthens the overall confusion and diffusion capabilities of the encryption scheme, making BQSSSD-IE suitable for secure and real-time image protection applications.
{"title":"Bit-level quad-block shuffling and sequential summing dispersing image encryption based on hyperchaotic 2D Euler Pi Crossed Sine Map","authors":"Omer Kocak , Uğur Erkan , Ismail Babaoglu","doi":"10.1016/j.dsp.2026.105941","DOIUrl":"10.1016/j.dsp.2026.105941","url":null,"abstract":"<div><div>Chaos-based image encryption methods strongly depend on the complexity and dynamic behavior of chaotic maps to achieve effective permutation and diffusion. In this study, a novel two-dimensional Euler Pi Crossed Sine (2D-EPICS) chaotic map is introduced, which exhibits hyperchaotic dynamics, wide chaotic ranges, and high sensitivity to initial conditions. The chaotic properties of the proposed map are rigorously analyzed using bifurcation diagrams, phase trajectories, Lyapunov exponents, and multiple entropy measures, including sample entropy, permutation entropy, Kolmogorov entropy, and C0 complexity, confirming its strong nonlinear behavior and unpredictability. Building upon this chaotic foundation, the Bit-Level Quad-Block Shuffling and Sequential Summing Dispersing Image Encryption (BQSSSD-IE) scheme is then developed. The encryption process consists of a bit-level permutation stage based on quadruple pixel blocks, followed by a bidirectional diffusion stage achieved through cumulative row-wise and column-wise summations, both driven by sequences generated from the 2D-EPICS map. Extensive security analyses and comparative evaluations demonstrate that the proposed method provides high entropy, low pixel correlation, strong resistance against statistical, differential, noise, and cropping attacks, and competitive computational efficiency. The enhanced dynamic behavior of the 2D-EPICS map significantly strengthens the overall confusion and diffusion capabilities of the encryption scheme, making BQSSSD-IE suitable for secure and real-time image protection applications.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"173 ","pages":"Article 105941"},"PeriodicalIF":3.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079696","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 : 2026-04-01Epub Date: 2026-01-13DOI: 10.1016/j.dsp.2026.105906
Haiyi Tong, Dekang Zhu, Zhou Zhang
This paper presents HAIR-GLMB, a Hybrid Appearance and IoU Reinforced Generalized Labeled Multi-Bernoulli (GLMB) filter tailored for multi-target tracking in challenging unmanned aerial vehicle (UAV) scenarios. To address frequent association ambiguities caused by dense target distributions, we propose an adaptive hybrid cost matrix that integrates Intersection-over-Union (IoU) spatial cues with appearance similarity. Specifically, an entropy-based adaptive weighting mechanism dynamically balances spatial and appearance information, thereby enhancing association reliability. We further develop a reinforced likelihood computation within the GLMB recursion, explicitly embedding spatial and appearance information into the update process. A motion-aware adaptive survival probability model is also proposed, effectively sustaining track continuity for inward-moving targets near the boundaries of the camera’s field of view. To improve efficiency, the Gibbs sampler is initialized with an assignment obtained by the Hungarian algorithm on the hybrid cost matrix, placing the Markov chain near high-probability regions and reducing sampling overhead under a limited computational budget. Experiments on challenging UAV benchmarks (VisDrone2019, UAVDT) show that HAIR-GLMB consistently outperforms a GLMB baseline relying only on IoU, yielding higher tracking accuracy, fewer identity switches, and reduced fragmentation.
{"title":"HAIR-GLMB: Hybrid appearance-IoU reinforced GLMB filter for UAV-based multi-target tracking","authors":"Haiyi Tong, Dekang Zhu, Zhou Zhang","doi":"10.1016/j.dsp.2026.105906","DOIUrl":"10.1016/j.dsp.2026.105906","url":null,"abstract":"<div><div>This paper presents HAIR-GLMB, a Hybrid Appearance and IoU Reinforced Generalized Labeled Multi-Bernoulli (GLMB) filter tailored for multi-target tracking in challenging unmanned aerial vehicle (UAV) scenarios. To address frequent association ambiguities caused by dense target distributions, we propose an adaptive hybrid cost matrix that integrates Intersection-over-Union (IoU) spatial cues with appearance similarity. Specifically, an entropy-based adaptive weighting mechanism dynamically balances spatial and appearance information, thereby enhancing association reliability. We further develop a reinforced likelihood computation within the GLMB recursion, explicitly embedding spatial and appearance information into the update process. A motion-aware adaptive survival probability model is also proposed, effectively sustaining track continuity for inward-moving targets near the boundaries of the camera’s field of view. To improve efficiency, the Gibbs sampler is initialized with an assignment obtained by the Hungarian algorithm on the hybrid cost matrix, placing the Markov chain near high-probability regions and reducing sampling overhead under a limited computational budget. Experiments on challenging UAV benchmarks (VisDrone2019, UAVDT) show that HAIR-GLMB consistently outperforms a GLMB baseline relying only on IoU, yielding higher tracking accuracy, fewer identity switches, and reduced fragmentation.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"173 ","pages":"Article 105906"},"PeriodicalIF":3.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981795","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 : 2026-04-01Epub Date: 2026-01-20DOI: 10.1016/j.dsp.2026.105949
Kexin Wang , Jian Zhang , Gang Xin , Jun Gao , Chengxian Ge , Yan Li
This paper proposes a multi-antenna based physical layer secure communication scheme and conducts a quantitative analysis of its secret key rate (SKR). Firstly, in order to meet the secure communication needs of long-distance terminals, a Rician fading multi-antenna secure communication model that conforms to the characteristics of satellite broadcast channels was established. Secondly, the upper and lower bounds of the SKR as well as its asymptotic limit under large-scale eavesdropping scenarios are derived, and the quantitative impacts of the antenna number ratio and channel gain ratio on the SKR limit are elucidated. To address the problem of limited antenna resources at the satellite, an optimal antenna number allocation strategy among legitimate terminals is further proposed. Simulations verify that tilting the antenna number allocation toward legitimate terminals with more prominent channel advantages can maximize the SKR. Compared with the uniform allocation of antenna numbers, the optimal allocation strategy can improve the SKR by up to 4.6% under certain scenario conditions. In addition, this strategy can achieve a positive SKR with only half the number of antennas. This scheme effectively addresses the issues of insufficient model adaptability and low resource efficiency in existing studies on satellite scenarios, providing a key theoretical basis for the design of satellite secure communication systems.
{"title":"Physical layer security analysis and resource optimization for satellite-terrestrial multi-antenna systems","authors":"Kexin Wang , Jian Zhang , Gang Xin , Jun Gao , Chengxian Ge , Yan Li","doi":"10.1016/j.dsp.2026.105949","DOIUrl":"10.1016/j.dsp.2026.105949","url":null,"abstract":"<div><div>This paper proposes a multi-antenna based physical layer secure communication scheme and conducts a quantitative analysis of its secret key rate (SKR). Firstly, in order to meet the secure communication needs of long-distance terminals, a Rician fading multi-antenna secure communication model that conforms to the characteristics of satellite broadcast channels was established. Secondly, the upper and lower bounds of the SKR as well as its asymptotic limit under large-scale eavesdropping scenarios are derived, and the quantitative impacts of the antenna number ratio and channel gain ratio on the SKR limit are elucidated. To address the problem of limited antenna resources at the satellite, an optimal antenna number allocation strategy among legitimate terminals is further proposed. Simulations verify that tilting the antenna number allocation toward legitimate terminals with more prominent channel advantages can maximize the SKR. Compared with the uniform allocation of antenna numbers, the optimal allocation strategy can improve the SKR by up to 4.6% under certain scenario conditions. In addition, this strategy can achieve a positive SKR with only half the number of antennas. This scheme effectively addresses the issues of insufficient model adaptability and low resource efficiency in existing studies on satellite scenarios, providing a key theoretical basis for the design of satellite secure communication systems.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"173 ","pages":"Article 105949"},"PeriodicalIF":3.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039117","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 : 2026-04-01Epub Date: 2026-01-13DOI: 10.1016/j.dsp.2026.105910
Dingli Lou, Tuo Fu, Defeng Chen, Huawei Cao
Dense false target jamming (DFTJ) is a typical form of active jamming that generates numerous false targets along the radar line of sight, significantly degrading the detection and tracking performance of radar systems. In multistatic radar systems with spatially separated receivers, jamming signals originating from the same source become highly correlated across various receivers after compensating for their delay and Doppler frequency differences, whereas true target echoes remain weakly correlated because of varying observation geometries. On the basis of these differences, we propose a method for extracting true target signals from jammed echoes. First, the jamming signals are aligned across different receivers by compensating for their amplitude, delay, and Doppler frequency differences. The compensated and pulse-compressed echoes are then stacked into a signal matrix, where the false targets remain nearly invariant across different columns and thus form a low-rank component, while the true targets exhibit amplitude, delay, and Doppler frequency variations, manifesting as sparse high-rank components. Based on this structural distinction, we formulate a robust principal component analysis problem for extracting the true target signals and solve it using the block coordinate descent approach. To satisfy real-time processing demands, we further develop a sequential processing-based version of the proposed method. The numerical simulation results demonstrate the effectiveness of the proposed method, which shows stable performance under different DFTJ strategies, jamming parameters and target characteristics.
{"title":"A true target signal extraction method for defending against dense false target jamming in multistatic radar systems","authors":"Dingli Lou, Tuo Fu, Defeng Chen, Huawei Cao","doi":"10.1016/j.dsp.2026.105910","DOIUrl":"10.1016/j.dsp.2026.105910","url":null,"abstract":"<div><div>Dense false target jamming (DFTJ) is a typical form of active jamming that generates numerous false targets along the radar line of sight, significantly degrading the detection and tracking performance of radar systems. In multistatic radar systems with spatially separated receivers, jamming signals originating from the same source become highly correlated across various receivers after compensating for their delay and Doppler frequency differences, whereas true target echoes remain weakly correlated because of varying observation geometries. On the basis of these differences, we propose a method for extracting true target signals from jammed echoes. First, the jamming signals are aligned across different receivers by compensating for their amplitude, delay, and Doppler frequency differences. The compensated and pulse-compressed echoes are then stacked into a signal matrix, where the false targets remain nearly invariant across different columns and thus form a low-rank component, while the true targets exhibit amplitude, delay, and Doppler frequency variations, manifesting as sparse high-rank components. Based on this structural distinction, we formulate a robust principal component analysis problem for extracting the true target signals and solve it using the block coordinate descent approach. To satisfy real-time processing demands, we further develop a sequential processing-based version of the proposed method. The numerical simulation results demonstrate the effectiveness of the proposed method, which shows stable performance under different DFTJ strategies, jamming parameters and target characteristics.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"173 ","pages":"Article 105910"},"PeriodicalIF":3.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039120","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 : 2026-04-01Epub Date: 2026-01-20DOI: 10.1016/j.dsp.2026.105931
Fan Yang , Junzhou Huo , Zhenxiang Guan , Hua Li , Zhang Cheng
To address the limitations of conventional object detection neck structures–particularly insufficient utilization of deep features and inadequate multi-scale interactions in micro-crack detection–this paper introduces the Multi-scale Feature Stereo Interaction Network, MSFI Net (Pro). At its core lies the innovative Multi-scale Stereo Feature Extraction (MSFE) Block, which constructs parallel spatial interaction pathways across three adjacent scales to comprehensively integrate feature maps from shallow, intermediate, and deep layers. Simultaneously, it introduces a gradient enhancement mechanism through dual hybrid residual connections, effectively preserving gradient flow and feature integrity. MSFI Net (Pro) further engineers a dual-stage fusion pipeline, cascading two MSFE Blocks for coarse refinement followed by precise fine-tuning of features. This synergizes with dense cross-layer connectivity to fortify information propagation. Moreover, the network incorporates shallower P2-layer feature maps, injecting less noisy geometric information that significantly bolsters the recognition capability for slender cracks. Validation on enhanced micro-crack datasets and the Severstal steel defect dataset demonstrates MSFI Net (Pro)’s consistent performance uplift for baseline models. Specifically, under micro-crack test conditions, it achieves a 0.144 improvement in AP50-95 for YOLOv11n, while simultaneously boosting recall rates and prediction confidence for micro-crack targets. Compared to mainstream SOTA neck-optimized models, MSFI Net (Pro) maintains significant performance advantages in detection precision, classification accuracy, and localization efficacy.
为了解决传统目标检测颈部结构的局限性，特别是在微裂纹检测中深层特征的利用不足和多尺度相互作用不足，本文介绍了多尺度特征立体相互作用网络MSFI Net （Pro）。其核心是创新的多尺度立体特征提取（Multi-scale Stereo Feature Extraction， MSFE）区块，构建三个相邻尺度的平行空间交互路径，全面整合浅层、中间层和深层特征图。同时，通过双混合残差连接引入梯度增强机制，有效保持梯度流和特征完整性。MSFI Net （Pro）进一步设计了双级融合管道，级联两个MSFE块进行粗细化，然后进行精确的特征微调。这与密集的跨层连接协同作用，以加强信息传播。此外，该网络结合了较浅的p2层特征图，注入较少噪声的几何信息，显著增强了对细长裂缝的识别能力。对增强微裂纹数据集和Severstal钢缺陷数据集的验证表明，MSFI Net （Pro）在基线模型上具有一致的性能提升。具体而言，在微裂纹测试条件下，YOLOv11n在AP50-95上实现了0.144的改进，同时提高了微裂纹目标的召回率和预测置信度。与主流SOTA颈部优化模型相比，MSFI Net （Pro）在检测精度、分类精度和定位效率方面保持了显著的性能优势。
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Pub Date : 2026-04-01Epub Date: 2026-01-08DOI: 10.1016/j.dsp.2026.105898
Meng Yin , Binghe Sun , Rugang Wang , Yuanyuan Wang , Feng Zhou , Xuesheng Bian
To address the challenges of weak features, susceptibility to complex background interference in infrared small targets, and the high computational cost of existing specialized detection models, this paper proposes the Dual-Domain Fusion and Class-Aware Self-supervised YOLO (DCS-YOLO). This framework leverages dual-domain feature fusion and class-aware self-supervised learning for semantic enhancement. During feature extraction, a Class-aware Self-supervised Semantic Fusion Module (CSSFM) utilizes a class-aware self-supervised architecture as a deep semantic guide for generating discriminative semantic features, thereby enhancing the perception of faint target characteristics. Additionally, a Dual-domain Aware Enhancement Module (A2C2f_DDA) is designed, which analyzes the high-frequency components of small targets and employs a spatial-frequency domain feature complementary fusion strategy to sharpen feature capture while suppressing background clutter. For feature upsampling and fusion, a Multi-dimensional Selective Feature Pyramid Network (MSFPN) employs a frequency-domain, spatial, and channel three-dimensional cooperative selection mechanism, integrated with deep semantic information, to enhance feature integration across dimensions and improve detection performance in complex scenes. Furthermore, lightweight components including GSConv, VoVGSCSP, and LSCD-Detect are incorporated to reduce computational complexity and model parameters. Comprehensive evaluations on the IRSTD-1K, RealScene-ISTD, and SIRST-v2 datasets demonstrate the effectiveness of the proposed algorithm, achieving [email protected] scores of 80.7%, 90.2%, and 93.3%, respectively. The results indicate that the algorithm effectively utilizes frequency-domain analysis and semantic enhancement, providing a powerful and efficient solution for infrared small target detection in complex scenarios while maintaining a favorable balance between accuracy and computational cost.
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