Signal Processing最新文献_第2页

Optimized transmit beamforming via covariance matrix transformation and combination in colocated MIMO radars 利用协方差矩阵变换和组合优化MIMO雷达发射波束形成

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-09-25 DOI: 10.1016/j.sigpro.2025.110312

Elahe Faghand, Esfandiar Mehrshahi

引用次数: 0

Sampling of graph signals based on joint time-vertex fractional Fourier transform 基于联合时间-顶点分数阶傅里叶变换的图信号采样

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-09-25 DOI: 10.1016/j.sigpro.2025.110309

Yu Zhang, Bing-Zhao Li

引用次数: 0

Spectral norm-based sparse arrays design: A matrix completion perspective 基于谱范数的稀疏阵列设计：矩阵补全视角

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-09-24 DOI: 10.1016/j.sigpro.2025.110310

Yi Li, Weijie Xia, Lingzhi Zhu, Jianjiang Zhou

引用次数: 0

Quickest detection of false data injection attack in distributed process tracking 分布式进程跟踪中快速检测虚假数据注入攻击

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-09-23 DOI: 10.1016/j.sigpro.2025.110294

Saqib Abbas Baba, Arpan Chattopadhyay

{"title":"Quickest detection of false data injection attack in distributed process tracking","authors":"Saqib Abbas Baba, Arpan Chattopadhyay","doi":"10.1016/j.sigpro.2025.110294","DOIUrl":"10.1016/j.sigpro.2025.110294","url":null,"abstract":"<div><div>This paper addresses the problem of detecting false data injection (FDI) attacks in distributed sensor networks without a fusion center, where agent nodes are interconnected via a communication graph and each employs a Kalman consensus information filter (KCIF) to estimate a global process. The state estimate at any sensor is computed from both the local observation history and information exchanged with its neighbors, enabling distributed information fusion. An adversary corrupts the observation at an unknown node and time. We propose quickest change detection (QCD) algorithms for both Bayesian (with known prior on attack time) and non-Bayesian (arbitrary attack time) settings, tailored to the non-i.i.d. nature of distributed consensus estimates. Importantly, the detection strategy relies on consensus estimates rather than the innovation, marking a departure from conventional approaches. In the Bayesian case, we develop a recursive computation of the non-trivial detection statistic at each node, despite the non-i.i.d. observations. For the non-Bayesian scenario, we employ a multiple hypothesis sequential probability ratio test for detection and identification, along with a window-limited generalized likelihood ratio (WL-GLR) algorithm for unknown attack strategies. Numerical results demonstrate that our methods significantly reduce detection delays compared to conventional <span><math><msup><mrow><mi>χ</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> detectors, offering improved resilience for distributed tracking systems.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110294"},"PeriodicalIF":3.6,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157392","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

Impact of communication link noise on distributed ATC stochastic optimization: Analysis and algorithmic enhancements 通信链路噪声对分布式ATC随机优化的影响：分析与算法改进

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-09-23 DOI: 10.1016/j.sigpro.2025.110289

Yishu Peng , Sheng Zhang , Zhengchun Zhou , Pengwei Wen , Fuyi Huang

引用次数: 0

Generative-contrastive learning for open set radar emitter identification 开放集雷达辐射源识别的生成-对比学习

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-09-23 DOI: 10.1016/j.sigpro.2025.110295

Dongming Wu, Junpeng Shi, Zhiyuan Zhang, Zhihui Li, Fangling Zeng

{"title":"Generative-contrastive learning for open set radar emitter identification","authors":"Dongming Wu, Junpeng Shi, Zhiyuan Zhang, Zhihui Li, Fangling Zeng","doi":"10.1016/j.sigpro.2025.110295","DOIUrl":"10.1016/j.sigpro.2025.110295","url":null,"abstract":"<div><div>In traditional radar emitter identification (REI) tasks, both the training and testing samples share the same distribution, and the model is trained solely to recognize known targets. However, in non-cooperative electromagnetic environments, unknown classes are often absent from the training data, which may be incorrectly classified as known classes. To address this issue, we propose an innovative Generative-contrastive Learning method for Open Set REI (GLOSE) from the perspective of feature space optimization. We first introduce a conditional generative model derived from diffusion to generate stable interpolated samples within the feature space, which are defined as an additional class to compress the coverage of known classes, thereby enhancing the capability to handle unknown space. Subsequently, we employ contrastive learning with an adaptive contrastive loss to further optimize the discriminative power of the feature space, which applies varying levels of intra-class similarity for different types of samples. Extensive experiments are conducted on a simulated radar emitter dataset based on intra-pulse unintentional modulation and a real-world automatic dependent surveillance-broadcast (ADS-B) dataset. The results demonstrate that the proposed method significantly improves the detection capability of unknown class samples while maintaining high classification accuracy for known classes.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110295"},"PeriodicalIF":3.6,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219935","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

Direct positioning of multiple targets based on electromagnetic vector sensors array 基于电磁矢量传感器阵列的多目标直接定位

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-09-22 DOI: 10.1016/j.sigpro.2025.110292

Ziheng Zhao, Rui Guo, Qi Liu, Shiyou Xu

{"title":"Direct positioning of multiple targets based on electromagnetic vector sensors array","authors":"Ziheng Zhao, Rui Guo, Qi Liu, Shiyou Xu","doi":"10.1016/j.sigpro.2025.110292","DOIUrl":"10.1016/j.sigpro.2025.110292","url":null,"abstract":"<div><div>Electromagnetic vector sensors (EMVSs) have gained significant attention in recent years, particularly in the field of source localization. These multi-component sensors are capable of simultaneously detecting both electric and magnetic field vector information, making them a key area of research and development. Traditional source localization methods usually estimate the source position by estimating intermediate parameters such as direction of arrival (DOA) or time of arrival (TOA) first, which involves multiple processing steps and is highly susceptible to noise. This paper employs EMVS for direct position determination (DPD), proposing distinct algorithms for line-of-sight (LOS) and multipath scenarios. In the LOS scenario, the inherent multidimensional structure of the data received by the EMVS is utilized to represent the received signal as a third-order tensor. Using the selected dual-component EMVS in this paper, data from multiple stations are concatenated into a large tensor, and the spatial location parameters of the target source are directly extracted through parallel factor (PARAFAC) decomposition. In the NLOS scenario, the data received at each station are first decorrelated, followed by direct extraction of the target source’s spatial location parameters using PARAFAC decomposition. The proposed methods eliminate the need for explicit estimation of intermediate parameters, perform localization directly in the tensor domain, and exhibit strong robustness and high capability in resolving multiple sources.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110292"},"PeriodicalIF":3.6,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157393","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

Extended sum and difference coarray design via motion strategies: Enhanced DOA estimation for non-circular signals with CADiS arrays 基于运动策略的扩展和差分共阵设计：基于CADiS阵列的非圆信号DOA估计

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-09-22 DOI: 10.1016/j.sigpro.2025.110293

Xiaolong Li, Xin Lai, Xiaofei Zhang

{"title":"Extended sum and difference coarray design via motion strategies: Enhanced DOA estimation for non-circular signals with CADiS arrays","authors":"Xiaolong Li, Xin Lai, Xiaofei Zhang","doi":"10.1016/j.sigpro.2025.110293","DOIUrl":"10.1016/j.sigpro.2025.110293","url":null,"abstract":"<div><div>Recently, the use of moving arrays for direction of arrival (DOA) estimation has garnered significant attention. Nevertheless, research on the application of moving arrays for DOA estimation of non-circular (NC) signals remains scarce. Coprime array with displaced subarray (CADiS) has gained considerable attention due to its large aperture and low mutual coupling characteristics. In this paper, we first investigate the expression for the sum coarray (SCA) generated by array motion. Based on the hole distribution in the CADiS configuration, we then propose a hole-free motion strategy. This method effectively fills all holes in the difference coarray (DCA) and SCA, resulting in a hole-free sum and difference coarray (SDCA). Furthermore, we propose a simplified array motion strategy to achieve hole-free SDCA with only single-step motion, and provide the closed-form expression for the maximum achievable uniform degrees of freedom (uDOFs). Finally, we demonstrate the superiority of the proposed methods in comparison to other arrays in terms of uDOFs, DOA estimation performance of NC signals, angular resolution, and the number of identifiable sources.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110293"},"PeriodicalIF":3.6,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157391","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

Adaptive dynamic guided image filtering for edge preservation and structure extraction 自适应动态引导图像滤波边缘保持和结构提取

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-09-20 DOI: 10.1016/j.sigpro.2025.110290

Jianwu Long, Shuang Chen, Yuanqin Liu, Kaixin Zhang, Qi Luo

{"title":"Adaptive dynamic guided image filtering for edge preservation and structure extraction","authors":"Jianwu Long, Shuang Chen, Yuanqin Liu, Kaixin Zhang, Qi Luo","doi":"10.1016/j.sigpro.2025.110290","DOIUrl":"10.1016/j.sigpro.2025.110290","url":null,"abstract":"<div><div>Image smoothing is a fundamental technique in the field of image processing. Different tasks require different smoothing characteristics. However, the smoothing behavior of a given operator is typically fixed and often cannot simultaneously satisfy multiple tasks, such as detail enhancement, clip-art compression artifacts removal, and structure–texture decomposition. To address this limitation, we propose an adaptive dynamic guided image filtering model that preserves edges and extracts structures. Specifically, a weight-guided map is first constructed using a local filter and then employed to iteratively guide a global optimization model. An adaptive penalty function is introduced to enhance flexibility, enabling the model to address diverse smoothing tasks by tuning the parameters accordingly. This allows it to tackle more challenging problems, such as precise structure–texture separation, which previous methods struggle with. Furthermore, we provide an efficient numerical solution to the proposed model and analyze the convergence of the iterative algorithm through experiments, demonstrating stable convergence under various parameter settings. To quantitatively evaluate the performance, we construct an Image Smoothing (IMS) dataset. Extensive experiments across various applications validate the effectiveness and superiority of the proposed algorithm.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110290"},"PeriodicalIF":3.6,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118298","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

Longtime coherent detection in GP-distributed clutter plus noise gp分布杂波加噪声中的长时间相干检测

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-09-20 DOI: 10.1016/j.sigpro.2025.110315

Xiao-Jun Zhang, Si-Yuan Chang, Peng-Lang Shui

{"title":"Longtime coherent detection in GP-distributed clutter plus noise","authors":"Xiao-Jun Zhang, Si-Yuan Chang, Peng-Lang Shui","doi":"10.1016/j.sigpro.2025.110315","DOIUrl":"10.1016/j.sigpro.2025.110315","url":null,"abstract":"<div><div>In high-resolution maritime radars, small target detection is an intractable task. As a recognized approach, longtime coherent integration encounters three difficulties: spatially heterogeneous sea clutter, heavy computational burden from pre-Doppler whitening methods, and non-negligible noise. This paper investigates a longtime adaptive post-Doppler coherent detection method in generalized Pareto (GP) distributed clutter plus noise, which requires fewer reference cells and lower computational cost. As the first contribution, the moving target detection (MTD) method in compound-Gaussian clutter is proved to be constant false alarm rate (CFAR) to clutter power and spectrum. In GP-distributed clutter, the cell-average/cell-median MTDs (CA/CM-MTDs) are shown to be worse than the near-optimum adaptive generalized likelihood ratio test linearly data-dependent threshold detector (GLRT-LTD), only when the shape parameter of clutter and integrated pulse number are small. As the second contribution, the CA/CM-MTDs are extended to GP-distributed clutter plus thermal noise, which exploits the effective shape parameter of the mixed interference and threshold dependent on clutter-to-noise ratio (CNR) at each Doppler bin. The CA/CM-MTDs are examined to be approximately CFAR in the mixed interference. Experiments using simulated and measured datasets are conducted to show that the CA/CM-MTDs obtain better performance than existing longtime coherent integration detectors in the mixed interference.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110315"},"PeriodicalIF":3.6,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157390","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