IET Signal Processing最新文献_第3页

Weighted Sum Secrecy Rate Optimization for Cooperative Double-IRS-Assisted Multiuser Network 双 IRS 辅助多用户合作网络的加权和保密率优化

IF 1.7 4区工程技术

IET Signal Processing Pub Date : 2024-04-20 DOI: 10.1049/2024/7768640

Shaochuan Yang, Kaizhi Huang, Hehao Niu, Yi Wang, Zheng Chu, Gaojie Chen, Zhen Li

{"title":"Weighted Sum Secrecy Rate Optimization for Cooperative Double-IRS-Assisted Multiuser Network","authors":"Shaochuan Yang, Kaizhi Huang, Hehao Niu, Yi Wang, Zheng Chu, Gaojie Chen, Zhen Li","doi":"10.1049/2024/7768640","DOIUrl":"10.1049/2024/7768640","url":null,"abstract":"<div>\u0000 <p>In this paper, we present a double-intelligent reflecting surfaces (IRS)-assisted multiuser secure system where the inter-IRS channel is considered. In particular, we maximize the weighted sum secrecy rate of the system by jointly optimizing the beamforming vector for transmitted signal and artificial noise at the base station (BS) and the cooperative phase shifts of two IRSs, under the constraints of transmission power at the BS and the unit-modulus phase shift of IRSs. To tackle the nonconvexity of the optimization problem, we first convert the objective function to its concave lower bound by utilizing a novel successive convex approximation technique, then solve the transformed problem iteratively by applying an alternating optimization method. The Lagrange dual method, Karush–Kuhn–Tucker conditions, and alternating direction method of multipliers are applied to develop a low-complexity solution for each subproblem. Finally, simulation results are provided to verify the advantages of the cooperative double-IRS scheme in comparison with the benchmark schemes.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/7768640","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140680602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CFA-Based Splicing Forgery Localization Method via Statistical Analysis 通过统计分析实现基于 CFA 的拼接伪造定位方法

IF 1.7 4区工程技术

IET Signal Processing Pub Date : 2024-04-16 DOI: 10.1049/2024/9929900

Lei Liu, Peng Sun, Yubo Lang, Jingjiao Li

{"title":"CFA-Based Splicing Forgery Localization Method via Statistical Analysis","authors":"Lei Liu, Peng Sun, Yubo Lang, Jingjiao Li","doi":"10.1049/2024/9929900","DOIUrl":"10.1049/2024/9929900","url":null,"abstract":"<div>\u0000 <p>The color filter array of the camera is an effective fingerprint for digital forensics. Most previous color filter array (CFA)-based forgery localization methods perform under the assumption that the interpolation algorithm is linear. However, interpolation algorithms commonly used in digital cameras are nonlinear, and their coefficients vary with content to enhance edge information. To avoid the impact of this impractical assumption, a CFA-based forgery localization method independent of linear assumption is proposed. The probability of an interpolated pixel value falling within the range of its neighboring acquired pixel values is computed. This probability serves as a means of discerning the presence and absence of CFA artifacts, as well as distinguishing between various interpolation techniques. Subsequently, curvature is employed in the analysis to select suitable features for generating the tampering probability map. Experimental results on the Columbia and Korus datasets indicate that the proposed method outperforms the state-of-the-art methods and is also more robust to various attacks, such as noise addition, Gaussian filtering, and JPEG compression with a quality factor of 90.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/9929900","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140698176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Robust Sidelobe Cancellation Algorithm Based on Beamforming Vector Norm Constraint 基于波束成形矢量规范约束的稳健侧叶消除算法

IF 1.7 4区工程技术

IET Signal Processing Pub Date : 2024-04-04 DOI: 10.1049/2024/7696638

Qing Wang, Huanding Qin, Kai Yang, Hao Wu, Fangmin He, Jin Meng

{"title":"A Robust Sidelobe Cancellation Algorithm Based on Beamforming Vector Norm Constraint","authors":"Qing Wang, Huanding Qin, Kai Yang, Hao Wu, Fangmin He, Jin Meng","doi":"10.1049/2024/7696638","DOIUrl":"10.1049/2024/7696638","url":null,"abstract":"<div>\u0000 <p>Sidelobe cancellation (SLC) is a well-established beamforming technique for mitigating interference, particularly in the context of satellite communication (SATCOM). However, traditional SLC suffers from the issue of partially canceling the desired signal at high signal-to-noise ratio (SNR), primarily due to unconstrained beamforming processing. Extensive research has been conducted to address this problem; however, existing algorithms have limitations such as dependence on knowledge of signal array vectors or number of interferers and involve high computational complexity. In this paper, we propose a robust SLC algorithm based on beamforming vector norm constraint. Our proposal offers a practical solution by only requiring knowledge of the earth station antenna gain and maximum auxiliary array gain to the desired signal, both of which are fully known. Furthermore, compared to traditional SLC, our proposed method introduces additional computational complexity that only scales linearly with the size of the auxiliary array. Simulation results demonstrate comparable performance between our proposed method and existing techniques such as diagonal loading and spatial degrees-of-freedom control-based algorithms.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/7696638","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140741261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Critical Design Considerations on Continuous Frequency Modulation Localization Systems 连续调频定位系统的关键设计考虑因素

IF 1.7 4区工程技术

IET Signal Processing Pub Date : 2024-04-04 DOI: 10.1049/2024/6664937

Belal Al-Qudsi, Mohammed El-Shennawy, Niko Joram, Marco Gunia, Frank Ellinger

{"title":"Critical Design Considerations on Continuous Frequency Modulation Localization Systems","authors":"Belal Al-Qudsi, Mohammed El-Shennawy, Niko Joram, Marco Gunia, Frank Ellinger","doi":"10.1049/2024/6664937","DOIUrl":"https://doi.org/10.1049/2024/6664937","url":null,"abstract":"<div>\u0000 <p>Real-time locating systems (RTLSs) suffer from clock synchronization inaccuracy among their distributed reference nodes. Conventional systems require periodic time synchronization and typically necessitate a two-way ranging (TWR) clock synchronization protocol to eliminate their measurement errors. Particularly, frequency-modulated continuous-wave (FMCW) time-based location systems pose unique design considerations on the TWR that have a significant impact on the quality of their measurements. In this paper, a valid operation design diagram is proposed for the case of an FMCW time-based TWR synchronization protocol. The proposed diagram represents an intersection area of two boundary curves that indicate the functionality of the system at a given frequency bandwidth, spectral length, and clock synchronization ambiguity. It presents an intuitive illustration of the measurement’s expected accuracy by indicating a larger intersection area for relaxed design conditions and vice versa. Furthermore, the absence of a working condition can easily be detected before proceeding with the actual system development. To demonstrate the feasibility of the proposed diagram, four scenarios with different design constraints were evaluated in a Monte-Carlo model of a basic TWR system. Moreover, an experimental measurement setup demonstrated the validity of the proposed diagram. Both the simulation and experimental outcomes show that the indicated valid conditions and the distribution of the measurements’ accuracy are in very good agreement.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/6664937","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Industrial Wireless Communication Security Using Deep Learning Architecture-Based Channel Frequency Response 利用基于深度学习架构的信道频率响应增强工业无线通信安全性

IF 1.7 4区工程技术

IET Signal Processing Pub Date : 2024-03-28 DOI: 10.1049/2024/8884688

Lamia Alhoraibi, Daniyal Alghazzawi, Reemah Alhebshi, Liqaa F. Nawaf, Fiona Carroll

{"title":"Enhancing Industrial Wireless Communication Security Using Deep Learning Architecture-Based Channel Frequency Response","authors":"Lamia Alhoraibi, Daniyal Alghazzawi, Reemah Alhebshi, Liqaa F. Nawaf, Fiona Carroll","doi":"10.1049/2024/8884688","DOIUrl":"10.1049/2024/8884688","url":null,"abstract":"<div>\u0000 <p>Wireless communication plays a crucial role in the automation process in the industrial environment. However, the open nature of wireless communication renders industrial wireless sensor networks susceptible to malicious attacks that impersonate authorized nodes. The heterogeneity of the wireless transmission channel, coupled with hardware and software limitations, further complicates the issue of secure authentication. This form of communication urgently requires a lightweight authentication technique characterized by low complexity and high security, as inadequately secure communication could jeopardize the evolution of industrial devices. These requirements are met through the introduction of physical layer authentication. This article proposes novel deep learning (DL) models designed to enhance physical layer authentication by autonomously learning from the frequency domain without relying on expert features. Experimental results demonstrate the effectiveness of the proposed models, showcasing a significant enhancement in authentication accuracy. Furthermore, the study explores the efficacy of various DL architecture settings and traditional machine learning approaches through a comprehensive comparative analysis.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/8884688","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140370967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning-Based Active Jamming Suppression for Radar Main Lobe 基于深度学习的雷达主瓣主动干扰抑制技术

IF 1.7 4区工程技术

IET Signal Processing Pub Date : 2024-03-15 DOI: 10.1049/2024/3179667

Yilin Jiang, Yaozu Yang, Wei Zhang, Limin Guo

{"title":"Deep Learning-Based Active Jamming Suppression for Radar Main Lobe","authors":"Yilin Jiang, Yaozu Yang, Wei Zhang, Limin Guo","doi":"10.1049/2024/3179667","DOIUrl":"10.1049/2024/3179667","url":null,"abstract":"<div>\u0000 <p>Due to the development of digital radio frequency memory (DRFM), active jamming against the main lobe of the radar has become mainstream in electronic warfare. The jamming infiltrates the radar receiver via the main lobe, covering up the target echo information. This greatly affects the detection, tracking, and localization of targets by radar. In this study, we consider jamming suppression based on the independence of RF features. First, two stacked sparse auto-encoders (SSAEs) are built to extract the RF characteristics and signal features carried out by the actual radar signal for subsequent jamming suppression. This method can effectively separate RF features from signal features, making the extracted RF features more efficient and accurate. Then, an SSAE-based jamming suppression auto-encoder (JSAE) is proposed; the mixed signal, including the radar signal, jamming signal, and noise, is input to JSAE for dimensionality reduction. Therefore, the radar signal and RF features, extracted by the two SSAEs in the previous step, are used to constrain the features of the reduced mixed signal. Moreover, we integrate the feature level and signal level to jointly achieve jamming suppression. The original radar signal is used to assist the radar signal reconstructed by the decoder. By first filtering out interference-related features and then reconstructing the signal, we can achieve better jamming suppression performance. Finally, the effectiveness of the proposed method is verified by simulating the actual collected data.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/3179667","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140238122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved Complex Convolutional Neural Network Based on SPIRiT and Dense Connection for Parallel MRI Reconstruction 基于 SPIRiT 和密集连接的改进型复杂卷积神经网络用于并行 MRI 重构

IF 1.7 4区工程技术

IET Signal Processing Pub Date : 2024-03-14 DOI: 10.1049/2024/7006156

Jizhong Duan, Xinmin Ren

{"title":"Improved Complex Convolutional Neural Network Based on SPIRiT and Dense Connection for Parallel MRI Reconstruction","authors":"Jizhong Duan, Xinmin Ren","doi":"10.1049/2024/7006156","DOIUrl":"10.1049/2024/7006156","url":null,"abstract":"<div>\u0000 <p>To accelerate the data acquisition speed of magnetic resonance imaging (MRI) and improve the reconstructed MR images’ quality, we propose a parallel MRI reconstruction model (SPIRiT-Net), which combines the iterative self-consistent parallel imaging reconstruction model (SPIRiT) with the cascaded complex convolutional neural networks (CCNNs). More specifically, this model adopts the SPIRiT model for reconstruction in the k-space domain and the cascaded CCNNs with dense connection for reconstruction in the image domain. Meanwhile, this model introduces the data consistency layers for better reconstruction in both the image domain and the k-space domain. The experimental results on two clinical knee datasets as well as the fastMRI brain dataset under different undersampling patterns show that the SPIRiT-Net model achieves better reconstruction performance in terms of visual effect, peak signal-to-noise ratio, and structural similarity over SPIRiT, Deepcomplex, and DONet. It will be beneficial to the diagnosis of clinical medicine.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/7006156","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140241599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lie Detection Technology of Bimodal Feature Fusion Based on Domain Adversarial Neural Networks 基于领域对抗神经网络的双模态特征融合谎言检测技术

IF 1.7 4区工程技术

IET Signal Processing Pub Date : 2024-03-02 DOI: 10.1049/2024/7914185

Yan Zhou, Feng Bu

{"title":"Lie Detection Technology of Bimodal Feature Fusion Based on Domain Adversarial Neural Networks","authors":"Yan Zhou, Feng Bu","doi":"10.1049/2024/7914185","DOIUrl":"10.1049/2024/7914185","url":null,"abstract":"<div>\u0000 <p>In the domain of lie detection, a common challenge arises from the dissimilar distributions of training and testing datasets. This causes a model mismatch, leading to a performance decline of the pretrained deep learning model. To solve this problem, we propose a lie detection technique based on a domain adversarial neural network employing a dual-mode state feature. First, a deep learning neural network was used as a feature extractor to isolate speech and facial expression features exhibited by the liars. The data distributions of the source and target domain signals must be aligned. Second, a domain-antagonistic transfer-learning mechanism is introduced to build a neural network. The objective is to facilitate feature migration from the training to the testing domain, that is, the migration of lie-related features from the source to the target domain. This method results in improved lie detection accuracy. Simulations conducted on two professional lying databases with different distributions show the superiority of the detection rate of the proposed method compared to an unimodal feature detection algorithm. The maximum improvement in detection rate was 23.3% compared to the traditional neural network-based detection method. Therefore, the proposed method can learn features unrelated to domain categories, effectively mitigating the problem posed by different distributions in the training and testing of lying data.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/7914185","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140082575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A DOA Estimation Method Based on an Improved Transformer Model for Uniform Linear Arrays with Low SNR 基于改进变压器模型的 DOA 估算方法，适用于低信噪比的均匀线性阵列

IF 1.7 4区工程技术

IET Signal Processing Pub Date : 2024-02-17 DOI: 10.1049/2024/6666395

Wei Wang, Lang Zhou, Kun Ye, Haixin Sun, Shaohua Hong

引用次数: 0

Small Sample Fiber Full State Diagnosis Based on Fuzzy Clustering and Improved ResNet Network 基于模糊聚类和改进的 ResNet 网络的小样本光纤全状态诊断

IF 1.7 4区工程技术

IET Signal Processing Pub Date : 2024-02-08 DOI: 10.1049/2024/5512014

Xiangqun Li, Jiawen Liang, Jinyu Zhu, Shengping Shi, Fangyu Ding, Jianpeng Sun, Bo Liu

{"title":"Small Sample Fiber Full State Diagnosis Based on Fuzzy Clustering and Improved ResNet Network","authors":"Xiangqun Li, Jiawen Liang, Jinyu Zhu, Shengping Shi, Fangyu Ding, Jianpeng Sun, Bo Liu","doi":"10.1049/2024/5512014","DOIUrl":"https://doi.org/10.1049/2024/5512014","url":null,"abstract":"The optical time domain reflectometer (OTDR) curve features of communication fibers exhibit subtle differences among their normal, subhealthy, and faulty operating states, making it challenging for existing machine learning-based fault diagnosis algorithms to extract these minute features. In addition, the OTDR curve field fault data are scarce, and data-driven deep neural network that needs a lot of data training cannot meet the requirements. In response to this issue, this paper proposes a communication fiber state diagnosis model based on fuzzy clustering and an improved ResNet. First, the pretrained residual network (ResNet) is modified by removing the classification layer and retaining the feature extraction layers. A global average pooling (GAP) layer is designed as a replacement for the fully connected layer. Second, fuzzy clustering, instead of the softmax classification layer, is employed in ResNet for its characteristic of requiring no subsequent data training. The improved model requires only a small amount of sample training to optimize the parameters of the GAP layer, thereby accommodating state diagnosis in scenarios with limited data availability. During the diagnosis process, the OTDR curves are input into the network, resulting in 512 features outputted in the GAP layer. These features are used to construct a feature vector matrix, and a dynamic clustering graph is formed using fuzzy clustering to realize the fiber state diagnosis. Through on-site data detection and validation, it has been demonstrated that the improved ResNet can effectively identify the full cycle of fiber states.","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139792540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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