Signal Processing最新文献_第8页

Masked variational transformer for complex clutter modeling and target detection 用于复杂杂波建模和目标检测的掩模变分变压器

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-13 DOI: 10.1016/j.sigpro.2025.110236

Lixing Shi , Xueling Liang , Wenchao Chen , Yaoqiang Liu , Tong Ding , Kun Qin , Bo Chen , Hongwei Liu

{"title":"Masked variational transformer for complex clutter modeling and target detection","authors":"Lixing Shi , Xueling Liang , Wenchao Chen , Yaoqiang Liu , Tong Ding , Kun Qin , Bo Chen , Hongwei Liu","doi":"10.1016/j.sigpro.2025.110236","DOIUrl":"10.1016/j.sigpro.2025.110236","url":null,"abstract":"<div><div>Weak target detection commonly encounters intense clutter interference, which overshadows weak signals and complicates the task. Taking advantage of the powerful data mining capability of neural networks, more and more deep learning-based methods are applied to radar target detection. Among the approaches, those founded upon unsupervised learning methodologies exhibit remarkable merit because they dispense with the requirement for target samples within the training step, making them highly applicable in practical target detecting scenarios. However, existing methods suffer from limitations in leveraging the range-Doppler (R-D) two-dimensional correlation and finely modeling in multiple clutter scenarios. In this paper, an unsupervised Transformer-based detector (TrDet) is proposed to break through the boundary of modeling capability. First, with the designed two-dimensional position embedding (2-DPE) and global query embedding (GQE) techniques, an unsupervised training strategy for R-D spectrum based on Transformer framework is utilized to achieve refined clutter modeling. Then, radar target detection is formulated as an out-of-distribution (OOD) detection task to mitigate clutter interference. Moreover, the masked variational Transformer-based detector (MVTrDet) is further proposed to prevent target information leakage when the target is in close proximity to the clutter in Doppler domain. Compared with several relative algorithms, our proposed methods are better suited for radar target detection in complex clutter environments. The experimental results derived from both measured data and simulated data verify the effectiveness of our proposed methods.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110236"},"PeriodicalIF":3.6,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144894884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Blind learning of the optimal fusion rule in wireless sensor networks 无线传感器网络中最优融合规则的盲学习

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-13 DOI: 10.1016/j.sigpro.2025.110238

J. Perez , I. Santamaria , A. Pagés-Zamora

{"title":"Blind learning of the optimal fusion rule in wireless sensor networks","authors":"J. Perez , I. Santamaria , A. Pagés-Zamora","doi":"10.1016/j.sigpro.2025.110238","DOIUrl":"10.1016/j.sigpro.2025.110238","url":null,"abstract":"<div><div>This work presents a general framework for blindly estimating the sensor parameters of decision-fusion systems over wireless sensor networks (WSNs). The sensors report their binary decisions to a fusion center (FC) through parallel binary symmetric channels. Then, the FC makes the final decision by combining the noisy sensor decisions according to a certain fusion rule.</div><div>We present an algorithm for the FC to blindly estimate the sensor parameters from the noisy sensor decisions received after a number of sensing periods. The algorithm covers a wide variety of situations that may arise in WSNs. For example, the algorithm is applicable when the FC knows in advance some of the parameters of some sensors, when it knows the true hypothesis for a subset of sensing periods, or when only a subset of sensors communicates their decisions in each sensing period.</div><div>Based on the estimates of the system parameters, optimal channel-aware fusion rules are derived considering the minimum Bayes risk criterion. Simulation results show that, after sufficient sensing periods, the estimates of the WSN parameters are accurate enough for the fusion rule to exhibit near-optimal detection performance.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110238"},"PeriodicalIF":3.6,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Mask-free beampattern shaping for active reconfigurable intelligent surface-aided transmit array 有源可重构智能表面辅助发射阵列的无掩模波束方向形

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-12 DOI: 10.1016/j.sigpro.2025.110232

Shengyao Chen , Qiuyue Zhang , Longyao Ran , Hongtao Li , Di Song , Feng Xi , Zhong Liu

{"title":"Mask-free beampattern shaping for active reconfigurable intelligent surface-aided transmit array","authors":"Shengyao Chen , Qiuyue Zhang , Longyao Ran , Hongtao Li , Di Song , Feng Xi , Zhong Liu","doi":"10.1016/j.sigpro.2025.110232","DOIUrl":"10.1016/j.sigpro.2025.110232","url":null,"abstract":"<div><div>This paper utilizes a closely-deployed active reconfigurable intelligent surface (RIS) to assist the transmit array for significantly reducing sidelobe levels without a mainlobe gain loss. To avoid pre-specifying a sub-optimal pattern mask, we jointly design the transmit weights and active RIS reflection coefficients by maximizing the ratio between minimal mainlobe gain and peak sidelobe level. Under the constraints of transmit power, total power consumption and maximum amplification factor of active RIS, and acceptable mainlobe ripple, we formulate the proposed beampattern shaping into a nonconvex constrained optimization problem. Then we customize an effective algorithm based on the alternating direction method of multipliers (ADMM) framework to tackle variable-coupled nonconvex constraints, wherein we specially solve the subproblems related to the transmit weights and active RIS reflection coefficients by leveraging consensus ADMM and quadratically constrained quadratic program with one constraint techniques simultaneously. Numerical results show that the proposed array yields markedly lower sidelobe levels and a slightly higher mainlobe gain than conventional and passive RIS-aided ones. The mechanism is that the beampattern component yielded by the reflection path has almost opposite phase and identical amplitude in the sidelobe region as that of the direct path, dramatically canceling the sidelobes.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110232"},"PeriodicalIF":3.6,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Optimizing latent space for effective radar target detection using variational auto-encoder 利用变分自编码器优化雷达目标有效探测的潜在空间

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-10 DOI: 10.1016/j.sigpro.2025.110235

Hongtao Ru, Shuwen Xu, Luxi Zhang, Penglang Shui

{"title":"Optimizing latent space for effective radar target detection using variational auto-encoder","authors":"Hongtao Ru, Shuwen Xu, Luxi Zhang, Penglang Shui","doi":"10.1016/j.sigpro.2025.110235","DOIUrl":"10.1016/j.sigpro.2025.110235","url":null,"abstract":"<div><div>Detecting small floating marine targets is a significant challenge in radar systems, as conventional neural networks fail to detect targets effectively due to the lack of discriminative prior information. To address this issue, this paper proposes a prior-guided, weakly supervised detector based on a multi-scale temporal variational auto-encoder (MST-VAE). First, radar returns are represented as one-dimensional sliding window Doppler sequences (SWDS) to enhance clutter–target separability. Then, an encoder with multi-scale and dilated convolutions is designed to match the Doppler irregularity of sea clutter and the periodic Doppler spikes of target returns in the SWDS. In addition, two clutter-focused loss functions are developed to ensure the model focuses on learning clutter properties without overfitting to simulated targets. Finally, three complementary anomaly scores are extracted from the MST-VAE and fused in a fast convex-hull detector. Experiments on measured radar data demonstrate that the proposed method outperforms a strong feature-based baseline, with average and maximum detection performance gains of 5.2% and 16.9%, respectively.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110235"},"PeriodicalIF":3.6,"publicationDate":"2025-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Accurate ternary polar linear canonical transform domain stereo image zero-watermarking 精确的三元极线性正则变换域立体图像零水印

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-09 DOI: 10.1016/j.sigpro.2025.110242

Xiangyang Wang, Dawei Wang, Jialin Tian, Panpan Niu

{"title":"Accurate ternary polar linear canonical transform domain stereo image zero-watermarking","authors":"Xiangyang Wang, Dawei Wang, Jialin Tian, Panpan Niu","doi":"10.1016/j.sigpro.2025.110242","DOIUrl":"10.1016/j.sigpro.2025.110242","url":null,"abstract":"<div><div>Stereo images have recently gained considerable attention due to their immersive nature, highlighting an urgent need for robust copyright protection mechanisms. However, most existing zero-watermarking algorithms are tailored for 2D images and do not adequately meet the unique requirements of stereo images. Moreover, current methods for zero-watermarking stereo images often fail to accurately represent and maintain the critical relationship between the left and right views, thereby limiting their effectiveness. To overcome these limitations, this paper proposes an innovative zero-watermarking method specifically designed for stereo images, which leverages an accurate ternary polar linear canonical transform (ATPLCT). We first introduced a new computational technique called the accurate polar linear canonical transform (APLCT) to address the numerical integration problems inherent in the polar linear canonical transform (PLCT). Next, we extend the APLCT using ternary number theory to develop the ATPLCT, which is specifically optimized for capturing stereo image characteristics. Finally, we propose a stereo image zero-watermarking strategy that integrates the ATPLCT with an asymmetric tent map. Comparative experiments and analyses show that our proposed method offers improved performance and greater robustness compared to existing approaches.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110242"},"PeriodicalIF":3.6,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144813921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

EEF: Energy score-guided feature enhancement fusion method for RGB and thermal infrared images object detection EEF：能量分值引导下的RGB和热红外图像目标检测特征增强融合方法

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-09 DOI: 10.1016/j.sigpro.2025.110231

Tianhao Hao , Jinfu Yang , Shaochen Zhang , Shuwen Wu

{"title":"EEF: Energy score-guided feature enhancement fusion method for RGB and thermal infrared images object detection","authors":"Tianhao Hao , Jinfu Yang , Shaochen Zhang , Shuwen Wu","doi":"10.1016/j.sigpro.2025.110231","DOIUrl":"10.1016/j.sigpro.2025.110231","url":null,"abstract":"<div><div>The full exploitation of the complementarity between different modalities is crucial for RGB and Thermal infrared images (RGB-T) object detection. However, most existing methods utilizing a traditional backbone to extract features often struggle to enhance the discriminability of features from different modalities, thereby restricting the representational capacity of fused features. We propose an energy score-guided feature enhancement fusion method (EEF) for RGB-T object detection. Firstly, we design an energy-based feature enhancement module (EFEM) that leverages the proposed channel energy score to assess the importance and reliability of feature channels to enhance the discriminability of features and make them more focused on the region of the object. Then, we introduce an Efficient Cross-modal Fusion Module (ECFM) to capture complementary information between modalities by utilizing the global feature interaction capability of attention mechanisms. Finally, we incorporate an adaptive feedback module (AFM), which utilizes the fused features as guidance information to obtain the corresponding learning weights for different modalities to enhance the representational capacity of original features. We thoroughly evaluate our approach on the LLVIP and FLIR datasets, achieving preferable results of 64.9% and 41.1% mAP. The promising results adequately demonstrate the effectiveness of EEF in RGB-T object detection tasks.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110231"},"PeriodicalIF":3.6,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Privacy-preserving federated graph neural network against poisoning attack 抗中毒攻击的隐私保护联邦图神经网络

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-09 DOI: 10.1016/j.sigpro.2025.110214

Guanghui He, Yanli Ren, Gang He, Guorui Feng, Xinpeng Zhang

{"title":"Privacy-preserving federated graph neural network against poisoning attack","authors":"Guanghui He, Yanli Ren, Gang He, Guorui Feng, Xinpeng Zhang","doi":"10.1016/j.sigpro.2025.110214","DOIUrl":"10.1016/j.sigpro.2025.110214","url":null,"abstract":"<div><div>Graph neural network (GNNs) has gradually moved from theory to application, however less attention has been paid to training for privacy preserving. Due to the particularity of the graph structure, the small disturbance of the graph will also reduce its performance. In order to resist poisoning attacks, this paper proposes a privacy defense strategy based on homomorphic encryption (HE). Specifically, we adopt HE to encrypt local embedding and generate global embedding under ciphertext in order to achieve the confidentiality of node embedding. Secondly, by calculating the cosine similarity between node features in ciphertext. Then the backpropagation process is divided into two parts, which are executed by the user and the server respectively to achieve the privacy of the intermediate gradient. During the whole process, the client’s private data and weights are always invisible to the server. Finally, the theoretical and experimental results show that the proposed protocol has a accuracy error of 1.2%–3.3% compared with the GNN model under plaintext data. Meanwhile, the accuracy of the model with the defense framework could be improved by 22%–27% compared to those models without the defense mechanisms under attack.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110214"},"PeriodicalIF":3.6,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Joint range-azimuth resolution limit for radar coincidence imaging based on spatial information theory 基于空间信息理论的雷达符合成像联合距离-方位分辨率极限

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-09 DOI: 10.1016/j.sigpro.2025.110234

Qing Xiong , Gong Zhang , Biao Xue , Dazhuan Xu , Henry Leung

{"title":"Joint range-azimuth resolution limit for radar coincidence imaging based on spatial information theory","authors":"Qing Xiong , Gong Zhang , Biao Xue , Dazhuan Xu , Henry Leung","doi":"10.1016/j.sigpro.2025.110234","DOIUrl":"10.1016/j.sigpro.2025.110234","url":null,"abstract":"<div><div>Resolution is a fundamental performance metric in radar imaging. In radar coincidence imaging (RCI), resolution is determined by the correlation between the reference radiation field and the target echo signal, leading to a coupling between range and azimuth resolutions. Additionally, noise significantly impacts the resolution. This paper develops a joint range-azimuth resolution limit (JRL) for RCI based on spatial information theory, providing a comprehensive resolution analysis under noisy conditions. Based on the imaging model of RCI, we derive the scattering information (SI) of two adjacent scatterers and decompose it into in-phase and quadrature components through Singular Value Decomposition (SVD). The JRL is defined as a critical state at which the quadrature component of SI reaches 1 bit. We derived the closed-form expression of the JRL using a second-order Taylor series expansion. Furthermore, the range resolution limit (RRL) and azimuth resolution limit (ARL) are derived from the closed-form JRL, which quantifies the relationship between the JRL and key factors, including the transmitting signal bandwidth, array aperture, number of transceiver antennas, and signal-to-noise ratio (SNR). Monte Carlo simulations validate the proposed JRL by comparing it with the resolution limits of conventional imaging methods in RCI.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110234"},"PeriodicalIF":3.6,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 detection method for moving targets based on the Radon Fourier transform and acceleration filter 基于Radon傅里叶变换和加速度滤波的运动目标检测方法

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-07 DOI: 10.1016/j.sigpro.2025.110228

Xijia Chen , Yongping Song , Jun Hu , Tian Jin , Fang Xu , Zengping Chen

{"title":"Efficient detection method for moving targets based on the Radon Fourier transform and acceleration filter","authors":"Xijia Chen , Yongping Song , Jun Hu , Tian Jin , Fang Xu , Zengping Chen","doi":"10.1016/j.sigpro.2025.110228","DOIUrl":"10.1016/j.sigpro.2025.110228","url":null,"abstract":"<div><div>For stable detection of High-speed small aircraft, long-term coherent accumulation is generally required, which presents challenges due to range and Doppler migration. The focus-before-detection method based on the generalized Radon Fourier transform (GRFT) has proven effective in addressing these issues. However, GRFT involves searching and compensating for motion parameters in a high-dimensional space, resulting in a substantial computational burden. This paper proposes a method that combines Radon Fourier transform (RFT) and an acceleration filter (AF), i.e. AF-RFT. Specifically, the RFT is first applied to the collected signals to eliminate the range migration (RM) caused by speed, projecting the target into range-speed space. Then, to address the Doppler frequency modulation (DFM) introduced by acceleration, an acceleration filter along the slow-time dimension is developed. This filter gathers the distributed target energy across speed units, enabling the target to focus in range-speed-acceleration space. Simulation results reveal that the proposed method effectively resolves RM and DFM, thereby improving detection performance while maintaining low computational burden.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110228"},"PeriodicalIF":3.6,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Understanding the SPICE method and beyond 理解SPICE方法及其他方法

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-07 DOI: 10.1016/j.sigpro.2025.110225

Mingyu Jiang, Heng Qiao

{"title":"Understanding the SPICE method and beyond","authors":"Mingyu Jiang, Heng Qiao","doi":"10.1016/j.sigpro.2025.110225","DOIUrl":"10.1016/j.sigpro.2025.110225","url":null,"abstract":"<div><div>The celebrated Sparse Iterative Covariance-based Estimation (SPICE) method is analyzed in this paper by capitalizing on its equivalent reformulations as certain compressed sensing programs. Existing compressed sensing theories fall short as the considered measurement matrices in these reformulations do not satisfy the critical technical conditions such as the restricted isometry property (RIP) and the associated weights lie outside the allowable value ranges covered by the available literature. The essential observation that motivates this paper is that the reformulations take overfitting solutions under particular conditions on the measurement matrix and weights. The overfitting behaviors of these reformulations are thoroughly examined for both single measurement vector (SMV) and multiple measurement vectors (MMV) cases with identical and different noise powers. With an additional orthogonal assumption on the measurement matrix, we provide the first lower error bounds of the overfitting solutions that are shown to be tight in certain scenarios. The fundamental insights obtained in this paper not only lead to an understanding of the SPICE method but also complement the current compressed sensing research by lifting the impractical restrictions for real problem settings. The theoretical claims are demonstrated by extensive numerical experiments.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110225"},"PeriodicalIF":3.6,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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