Signal Processing最新文献_第5页

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

Unbiased initial phase estimation for real-valued sinusoids with known frequency via spectral leakage compensation 基于频谱泄漏补偿的已知频率实值正弦波的无偏初始相位估计

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-07 DOI: 10.1016/j.sigpro.2025.110227

Zhe Zhao , Linyue Zhang , Feng Zhang

{"title":"Unbiased initial phase estimation for real-valued sinusoids with known frequency via spectral leakage compensation","authors":"Zhe Zhao , Linyue Zhang , Feng Zhang","doi":"10.1016/j.sigpro.2025.110227","DOIUrl":"10.1016/j.sigpro.2025.110227","url":null,"abstract":"<div><div>This paper investigates the problem of initial phase estimation for a real-valued sinusoidal signal with known frequency. We analyze the bias of the conventional maximum likelihood estimator (MLE) and show that it primarily arises from spectral leakage in the discrete Fourier transform (DFT). Based on this observation, we propose a novel unbiased estimator that eliminates the influence of spectral leakage, thereby achieving unbiased estimation of the initial phase. From a theoretical perspective, we prove that a statistic related to the proposed unbiased estimator is not complete. As a result, it is not possible to theoretically establish that the proposed estimator is the minimum variance unbiased estimator (MVUE) within the framework of the Lehmann–Scheffé theorem, due to the incompleteness of the statistic. Nevertheless, Monte Carlo simulations are conducted to evaluate the performance of the proposed estimator under various frequencies, initial phases, and signal-to-noise ratio (SNR) conditions. The results show that the proposed method consistently achieves unbiased estimation and yields a variance close to the Cramér–Rao lower bound (CRLB) in all tested scenarios.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110227"},"PeriodicalIF":3.6,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144813920","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

Minimally informed linear discriminant analysis: Training an LDA model with unlabelled data 最小知情线性判别分析：用未标记数据训练LDA模型

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-07 DOI: 10.1016/j.sigpro.2025.110226

Nicolas Heintz , Tom Francart , Alexander Bertrand

{"title":"Minimally informed linear discriminant analysis: Training an LDA model with unlabelled data","authors":"Nicolas Heintz , Tom Francart , Alexander Bertrand","doi":"10.1016/j.sigpro.2025.110226","DOIUrl":"10.1016/j.sigpro.2025.110226","url":null,"abstract":"<div><div>Linear Discriminant Analysis (LDA) is one of the oldest and most popular linear methods for supervised classification problems. Computing the optimal LDA projection vector requires calculating the average and covariance of the feature vectors of each class individually, which necessitates class labels to estimate these statistics from the data. In this paper we demonstrate that, if some minor prior information is available, it is possible to compute the exact projection vector from LDA models based on unlabelled data. More precisely, we show that either one of the following three pieces of information is sufficient to compute the LDA projection vector if only unlabelled data are available: (1) the class average of one of the two classes, (2) the difference between both class averages (up to a scaling), or (3) the class covariance matrices (up to a scaling). These theoretical results are validated in numerical experiments, demonstrating that this minimally informed Linear Discriminant Analysis (MILDA) model closely approximates the solution of a supervised LDA model, even on high-dimensional, poorly separated or extremely imbalanced data. Furthermore, we show that the MILDA projection vector can be computed in a closed form with a computational cost comparable to LDA and is able to quickly adapt to non-stationary data, making it well-suited to use as an adaptive classifier that is continuously retrained on (unlabelled) streaming data.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110226"},"PeriodicalIF":3.6,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852208","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

Error bound for two-dimensional DOA joint estimation in RIS assisted wireless network RIS辅助无线网络中二维DOA联合估计误差界

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-07 DOI: 10.1016/j.sigpro.2025.110229

Cuimin Pan , Xiangbin Yu , Jingjing Pan , Han Zhang

{"title":"Error bound for two-dimensional DOA joint estimation in RIS assisted wireless network","authors":"Cuimin Pan , Xiangbin Yu , Jingjing Pan , Han Zhang","doi":"10.1016/j.sigpro.2025.110229","DOIUrl":"10.1016/j.sigpro.2025.110229","url":null,"abstract":"<div><div>Reconfigurable intelligent surface (RIS) has been a crucial enabler for improving wireless localization accuracy through effectively controlling radio propagation environment. This paper investigates the performance bound for RIS-assisted two-dimensional (2D) direction of arrival (DOA) joint estimation. While the Cramér–Rao lower bound (CRLB) serves as the fundamental performance benchmark for mean square error, it is only asymptotically tight. To this end, an information-theory performance bound termed 2D DOA entropy error (2D-DEE) is proposed through statistical characterization of angle estimation uncertainty. Specifically, the joint <em>a posteriori</em> probability density function (PDF) of 2D DOA is first derived incorporating the uniform and independent <em>a priori</em> distributions of DOAs. Based on this joint <em>a posteriori</em> PDF, the <em>a posteriori</em> entropy is then normalized for different signal-to-noise ratio (SNR) to derive an explicit expression for 2D-DEE. For further insight, the asymptotic expression for entropy errors of 1D DOA and 2D DOA are analyzed in high SNR region. Extensive numerical results validate the accuracy of theoretical analysis and demonstrate that the derived 2D-DEE is able to maintain tight over wider range of SNR in evaluating and predicting 2D DOA estimation performance.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110229"},"PeriodicalIF":3.6,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809427","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