IEEE Signal Processing Letters最新文献_第2页

Multi Target Localization With Block Orthogonal Least Squares for Multistatic MIMO Radars 基于块正交最小二乘的多基地MIMO雷达多目标定位

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-28 DOI: 10.1109/LSP.2025.3565168

Martin Willame;Gilles Monnoyer;Hasan Can Yildirim;François Horlin;Jérôme Louveaux

{"title":"Multi Target Localization With Block Orthogonal Least Squares for Multistatic MIMO Radars","authors":"Martin Willame;Gilles Monnoyer;Hasan Can Yildirim;François Horlin;Jérôme Louveaux","doi":"10.1109/LSP.2025.3565168","DOIUrl":"https://doi.org/10.1109/LSP.2025.3565168","url":null,"abstract":"Recently, there has been a growing interest in multistatic radar configurations to improve the localization of multiple targets. Theoretically, the maximum likelihood (ML) approach enables to fuse the information provided by each radar pair to localize the different targets. However, it involves a multi-dimensional search process whose complexity exponentially grows with the number of targets. Consequently, heuristic methods, notably including the block orthogonal matching pursuit (BOMP), have been used in the multistatic radar context to approach the ML estimation greedily. Interestingly, the more accurate block orthogonal least squares (BOLS) method has not been studied in this context because the performance improvement is usually low in regard to its computational complexity. In this work, we investigate the application of BOLS to an angle-based localization of multiple targets using a multistatic multiple-input and multiple-output (MIMO) radar. First, an efficient implementation of BOLS is presented reducing its computational complexity. Then, using Monte Carlo simulations, we show evidence of the significant advantage of this efficient implementation of BOLS over BOMP in this scenario featuring highly correlated signals. The impact of radar parameters on the localization root mean square error and on the computational complexity of both algorithms is studied.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1990-1994"},"PeriodicalIF":3.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072823","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

Distributed Secure State Estimation Against Stealthy Attacks 针对隐身攻击的分布式安全状态估计

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-28 DOI: 10.1109/LSP.2025.3564886

Yan Yu;Wen Yang;Hongbo Yuan;Longyu Li;Chao Yang

引用次数: 0

Instantaneous Frequency Estimation via Ridge Detection in Polynomial Time and Space 基于多项式时间和空间脊检测的瞬时频率估计

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-28 DOI: 10.1109/LSP.2025.3564882

Igor Djurović

引用次数: 0

Conditional Diffusion Model for Skeleton-Based Gesture Recognition With Severe Occlusions 基于骨骼的严重遮挡手势识别条件扩散模型

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-23 DOI: 10.1109/LSP.2025.3563445

Jinting Liu;Minggang Gan;Yao Du;Keyi Guan;Jia Guo

引用次数: 0

A Convex Combination-Based Distributed Momentum Methods Over Directed Graphs 一种基于凸组合的有向图分布动量方法

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-23 DOI: 10.1109/LSP.2025.3563722

Siyuan Huang;Juan Gao;Qiao-Li Dong;Cuijie Zhang

引用次数: 0

QELDBA: Query-Efficient and Low Distortion Black-Box Attack for Brainprint Recognition 基于查询效率和低失真的脑印识别黑盒攻击

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-22 DOI: 10.1109/LSP.2025.3563446

Jingsheng Qian;Hangjie Yi;Honggang Liu;Xuanyu Jin;Wanzeng Kong

{"title":"QELDBA: Query-Efficient and Low Distortion Black-Box Attack for Brainprint Recognition","authors":"Jingsheng Qian;Hangjie Yi;Honggang Liu;Xuanyu Jin;Wanzeng Kong","doi":"10.1109/LSP.2025.3563446","DOIUrl":"https://doi.org/10.1109/LSP.2025.3563446","url":null,"abstract":"While various deep learning techniques for electroencephalogram (EEG)-based brainprint recognition have achieved considerable success, these models remain vulnerable to adversarial attacks. However, existing black-box attack methods suffer from an inherent trade-off between query efficiency and distortion level. To address this challenge and further investigate the security risks of brainprint recognition systems in real-world black-box scenarios, we propose a query-efficient, low-distortion black-box attack method that targets the high-frequency components of EEG signals. Our approach innovatively selects sparse sampling points to estimate more accurate gradient information and leverages historical gradients to guide the prioritization of important points, thereby accelerating the attack process. The perturbations are applied in the high-frequency domain of the EEG signal to enhance stealth and effectiveness. Extensive experiments under black-box settings demonstrate that our method achieves state-of-the-art performance across two datasets and four models. Compared to existing methods, our approach significantly improves attack success rates while reducing the number of queries and minimizing distortion to imperceptible levels, thus achieving a superior balance between query efficiency and perturbation stealth.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"2020-2024"},"PeriodicalIF":3.2,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144099972","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

Asynchronous Voice Anonymization by Learning From Speaker-Adversarial Speech 基于对抗性语音学习的异步语音匿名化

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-22 DOI: 10.1109/LSP.2025.3563306

Rui Wang;Liping Chen;Kong Aik Lee;Zhen-Hua Ling

引用次数: 0

Vision Mamba Distillation for Low-Resolution Fine-Grained Image Classification 用于低分辨率细粒度图像分类的视觉曼巴蒸馏

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-22 DOI: 10.1109/LSP.2025.3563441

Yao Chen;Jiabao Wang;Peichao Wang;Rui Zhang;Yang Li

{"title":"Vision Mamba Distillation for Low-Resolution Fine-Grained Image Classification","authors":"Yao Chen;Jiabao Wang;Peichao Wang;Rui Zhang;Yang Li","doi":"10.1109/LSP.2025.3563441","DOIUrl":"https://doi.org/10.1109/LSP.2025.3563441","url":null,"abstract":"Low-resolution fine-grained image classification has recently made significant progress, largely thanks to the super-resolution techniques and knowledge distillation methods. However, these approaches lead to an exponential increase in the number of parameters and computational complexity of models. In order to solve this problem, in this letter, we propose a Vision Mamba Distillation (ViMD) approach to enhance the effectiveness and efficiency of low-resolution fine-grained image classification. Concretely, a lightweight super-resolution vision Mamba classification network (SRVM-Net) is proposed to improve its capability for extracting visual features by redesigning the classification sub-network with Mamba modeling. Moreover, we design a novel multi-level Mamba knowledge distillation loss to boost the performance. The loss can transfer prior knowledge obtained from a High-resolution Vision Mamba classification Network (HRVM-Net) as a teacher into the proposed SRVM-Net as a student. Extensive experiments on seven public fine-grained classification datasets related to benchmarks confirm our ViMD achieves a new state-of-the-art performance. While having higher accuracy, ViMD outperforms similar methods with fewer parameters and FLOPs, which is more suitable for embedded device applications.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1965-1969"},"PeriodicalIF":3.2,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072818","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

Complex Singular Spectrum Analysis Leveraging Adaptive Taper Windows for Enhancing Mode Reconstruction From Multivariate Signals 利用自适应锥窗增强多元信号模式重构的复奇异谱分析

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3562823

Jialiang Gu;Kevin Hung;Bingo Wing-Kuen Ling;Daniel Hung-Kay Chow;Yang Zhou

引用次数: 0

ASSMark: Dual Defense Against Speech Synthesis Attack via Adversarial Robust Watermarking 基于对抗性鲁棒水印的双重防御语音合成攻击

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3562817

Yulin He;Hongxia Wang;Yiqin Qiu;Hao Cao

{"title":"ASSMark: Dual Defense Against Speech Synthesis Attack via Adversarial Robust Watermarking","authors":"Yulin He;Hongxia Wang;Yiqin Qiu;Hao Cao","doi":"10.1109/LSP.2025.3562817","DOIUrl":"https://doi.org/10.1109/LSP.2025.3562817","url":null,"abstract":"Given the widespread dissemination of digital audio and the advancements in speech synthesis technologies, protecting audio copyright has become a critical issue. Although watermarks play an important role in copyright verification and forensic analysis, they are insufficient to proactively defend against malicious speech synthesis. To address this issue, we introduce a novel adversarial speech synthesis watermarking mechanism (ASSMark), which simultaneously traces the audio copyright and disrupts the speech synthesis models by embedding robust adversarial watermarks in a one-time manner. Specifically, we design a unified training framework that models the embedding of watermarks and adversarial perturbations as collaborative tasks. This approach allows for the fine-tuning of any robust watermark into an adversarial watermark, resulting in watermarked audio that can effectively defend against unauthorized speech synthesis attacks. Experimental results demonstrate that ASSMark achieves over 90% protection rate even to unknown black-box models. Compared to simplistic two-step protection methods, it not only effectively resists synthesis attacks but also achieves superior watermark extraction accuracy and speech quality, offering an outstanding solution for protecting audio copyright.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1870-1874"},"PeriodicalIF":3.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929842","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