IET Signal Processing最新文献

Optimization of Target Detection Performance in Rotating Multichannel Radar Systems 旋转多通道雷达系统目标检测性能优化

IF 1.4 4区工程技术

IET Signal Processing Pub Date : 2025-10-09 DOI: 10.1049/sil2/3429170

Zheyi Liu, Yifeng Wu, Kai Luo, Lei Zhang, Jianxin Wu, Jia Duan

{"title":"Optimization of Target Detection Performance in Rotating Multichannel Radar Systems","authors":"Zheyi Liu, Yifeng Wu, Kai Luo, Lei Zhang, Jianxin Wu, Jia Duan","doi":"10.1049/sil2/3429170","DOIUrl":"https://doi.org/10.1049/sil2/3429170","url":null,"abstract":"In the application of automotive radar, vehicle turning is a critical scenario. The introduction of rotational angular velocity causes Doppler shifts in forward-facing speed radars, leading to defocusing of multichannel echo data. This paper attempts to propose a method for compensating and correcting the rotational angular velocity to focus the energy across channels by compensating for the speed in each channel. Considering the rotational angles of the vehicle-mounted platform, the real incident angle of an unknown target must be accounted for since the main beam covers a relatively large range. Therefore, we include the target’s angle of arrival, conducting a search within the main beam range during detection. When the true angle of arrival of the target is identified, detection performance reaches its optimal level. After, employing the rotational speed search method proposed in this paper, under a four-channel rotating radar platform, the signal-to-noise ratio (SNR) for target detection is enhanced by approximately 15 dB, which aligns with the theoretical gain from coherent accumulation of SNR derived in the subsequent sections of the paper. Furthermore, obtaining the exact value of the platform’s rotational speed may not always be easy. Hence, we incorporate the rotational speed into the search range. After, performing a two-dimensional search, the peak of the detection performance graph corresponds to the true rotational speed of the vehicle-mounted platform and the angle within the main beam range where the target is located. Conversely, knowing the prior information of any dimension in the two-dimensional search can expedite and improve the detection performance of the other dimension’s parameters.","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2025 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sil2/3429170","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145272474","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

Weak Coherent Light Interference Heterodyne Detection Based on Time-Domain Signal Analysis 基于时域信号分析的弱相干光干涉外差检测

IF 1.4 4区工程技术

IET Signal Processing Pub Date : 2025-10-01 DOI: 10.1049/sil2/9918739

Hui Shen, Yousen Li

{"title":"Weak Coherent Light Interference Heterodyne Detection Based on Time-Domain Signal Analysis","authors":"Hui Shen, Yousen Li","doi":"10.1049/sil2/9918739","DOIUrl":"https://doi.org/10.1049/sil2/9918739","url":null,"abstract":"Weak coherent light interference heterodyne detection is the theoretical basis for fiber optic gyroscopes, optical coherence tomography, and optical time-domain reflectometers. Classical statistical optics provides the signal model for weak coherent light interference. However, this theory does not describe signal acquisition and nonpolarization, which are significant in the analysis of heterodyne detection frequency, coherent length, and polarization mode dispersion (PMD). Consequently, it has difficulty solving signal processing problems related to coherent frequency and length analysis. This article proposed a time-domain signal analysis method. The approach can describe the practical signal acquisition and the polarized direction interference and accurately obtain coherent frequency and length on weak coherent light interference heterodyne detection signals by integrating the interference signals of monochromatic light within the linewidth of weak coherent light. We obtained the final mode of the signals using MATLAB. We established an experimental system to validate the practical value of the approach in signal processing. The average deviation between the experimental and theoretical coherent frequency and length is 120.6 Hz/0.48% and −0.0072 μm/−0.06%, respectively. Compared with existing theory, the proposed method is advantageous for describing detector acquisition and has practical value in heterodyne detection analysis. The proposed method can be widely applied to the systems based on weak coherent interference.","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2025 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sil2/9918739","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223990","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

WA-BSN: Self-Supervised Real-World Image Denoising Based on Wavelet-Adaptive Blind Spot Network 基于小波自适应盲点网络的自监督真实世界图像去噪

IF 1.4 4区工程技术

IET Signal Processing Pub Date : 2025-09-23 DOI: 10.1049/sil2/4971725

Hezhen Xia, Hongyi Liu, Zhihui Wei

{"title":"WA-BSN: Self-Supervised Real-World Image Denoising Based on Wavelet-Adaptive Blind Spot Network","authors":"Hezhen Xia, Hongyi Liu, Zhihui Wei","doi":"10.1049/sil2/4971725","DOIUrl":"10.1049/sil2/4971725","url":null,"abstract":"Blind spot network (BSN) has gained increasing attention with its state-of-the-art performance in self-supervised image denoising. However, most existing BSN models are based on an unrealistic assumption of noise independence and use isotropic mask convolutions, which can lead to the loss of structural details in the denoised image. To address these limitations, we consider the spatially correlated noise and introduce directional adaptive downsampling and mask convolutions to the wavelet domain, resulting in a novel self-supervised denoising method called wavelet-adaptive BSN (WA-BSN). Specifically, we design the direction-adaptive pixel-shuffle downsamplings (PDs) and apply them to the wavelet decomposition subbands, where the spatial-correlated noise is eliminated and the inherent structure is well preserved in the wavelet domain. Then, based on the geometric direction of the wavelet subimages, we propose four shape-adaptive mask convolutions of a smaller size for each wavelet subband in WA-BSN. This enables adaptive pixel prediction within a structural neighborhood for each subband with reduced training time. Finally, total variation (TV) is added to the loss function to further preserve the edges. The results on public real-world datasets demonstrate that our method significantly outperforms existing self-supervised denoising methods and achieves great efficiency.","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2025 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sil2/4971725","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145122656","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 Deep Reinforcement Learning–Based Low Earth Orbit Satellite-Enhanced Mobile Edge Computing Framework for Efficient Task Off-Loading 基于深度强化学习的低地球轨道卫星增强移动边缘计算框架高效任务卸载

IF 1.4 4区工程技术

IET Signal Processing Pub Date : 2025-09-08 DOI: 10.1049/sil2/9674618

Erlong Wei, Yihong Wen, Xuebo Liu

{"title":"A Deep Reinforcement Learning–Based Low Earth Orbit Satellite-Enhanced Mobile Edge Computing Framework for Efficient Task Off-Loading","authors":"Erlong Wei, Yihong Wen, Xuebo Liu","doi":"10.1049/sil2/9674618","DOIUrl":"10.1049/sil2/9674618","url":null,"abstract":"With the rapid advancement of the Internet of Things (IoTs) and 6G technologies, traditional terrestrial networks are becoming less capable of supporting demanding computational tasks. This limitation stems from their restricted coverage and poor adaptability to changing environmental conditions. Low earth orbit (LEO) satellite networks offer global coverage. However, existing mobile edge computing (MEC) frameworks struggle with unstable links, high decision complexity, and limited real-time performance. To overcome these challenges, this paper proposes a LEO satellite-enhanced MEC off-loading architecture based on improved multiagent deep reinforcement learning (MADRL). By integrating ground terminals, LEO satellite edge servers, cloud servers into a three-tier collaborative system, and introducing an independent Q-value mechanism, the proposed method jointly optimizes task off-loading and resource allocation in dynamic environments. This design reduces algorithm complexity and enhances decision flexibility. Experimental results show that the proposed method outperforms baseline approaches in end-to-end latency, energy efficiency, and convergence speed, while maintaining robust performance under varying satellite densities and user workloads. These results demonstrate the potential of the proposed approach for efficient task off-loading in dynamic 6G scenarios.","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2025 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sil2/9674618","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145008000","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

Development of RF Hardware and Point Cloud Processing Method for Phased Array Millimeter Wave Radar 相控阵毫米波雷达射频硬件发展及点云处理方法

IF 1.4 4区工程技术

IET Signal Processing Pub Date : 2025-08-07 DOI: 10.1049/sil2/3049323

Zihang Yan, Hua Zhang, Bo Yan, Jingrong Sun

{"title":"Development of RF Hardware and Point Cloud Processing Method for Phased Array Millimeter Wave Radar","authors":"Zihang Yan, Hua Zhang, Bo Yan, Jingrong Sun","doi":"10.1049/sil2/3049323","DOIUrl":"10.1049/sil2/3049323","url":null,"abstract":"The development of smart transportation has raised the demand for perception and detection of vehicle targets on the road, and compared to traditional methods, such as video cameras, millimeter wave radar applications are becoming increasingly widespread. This article mainly focuses on the research of phased array millimeter wave radar, introduces the principles of beamforming and scanning, designs microstrip array antennas, and develops radar hardware RF boards using a four-chip cascade approach. Further elaborating on the data acquisition process of phased array millimeter wave radar in detecting vehicle targets, based on the obtained point cloud data, combined with the target data and point cloud characteristics under phased array millimeter wave radar, a target point cloud clustering method using the concept of region growing is proposed. Finally, through actual testing and comparison with other clustering algorithms, the superiority of this method in clustering accuracy and processing time was verified. This method can effectively solve the problem of two targets easily converging into one target when they are close, further improving the detection and tracking performance of phased array millimeter wave radar for vehicle targets.","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2025 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sil2/3049323","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145122624","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

Training Sample Selection Based on SAR Images Quality Evaluation With Multi-Indicators Fusion 基于多指标融合SAR图像质量评价的训练样本选择

IF 1.4 4区工程技术

IET Signal Processing Pub Date : 2025-07-30 DOI: 10.1049/sil2/1612434

Pengcheng Wang, Huanyu Liu, Junbao Li

引用次数: 0

Automatic Epilepsy Seizure Classification Using EEG Signals Based on the CNN-LSTM Model 基于CNN-LSTM模型的脑电信号癫痫发作自动分类

IF 1.4 4区工程技术

IET Signal Processing Pub Date : 2025-07-18 DOI: 10.1049/sil2/7543401

C. Ruth Vinutha, M. S. P. Subathra, S. Thomas George, Geno Peter, Albert Alexander Stonier, N. J. Sairamya, J. Prasanna, Vivekananda Ganji

{"title":"Automatic Epilepsy Seizure Classification Using EEG Signals Based on the CNN-LSTM Model","authors":"C. Ruth Vinutha, M. S. P. Subathra, S. Thomas George, Geno Peter, Albert Alexander Stonier, N. J. Sairamya, J. Prasanna, Vivekananda Ganji","doi":"10.1049/sil2/7543401","DOIUrl":"10.1049/sil2/7543401","url":null,"abstract":"Epilepsy is a neurological disorder characterized by frequent seizures and abnormal brain activity. It is typically diagnosed by examining electroencephalogram (EEG) recordings from epilepsy patients. Early detection and careful monitoring of children with epilepsy are crucial to preventing damaging spikes before the onset of the first seizure. Traditionally, this condition is examined manually by medical experts, a time-consuming process, especially during prolonged recordings. Therefore, an automated method for diagnosing focal (abnormal) EEG signals is essential. This study proposes an efficient model to classify and provide insights into focal and nonfocal stages. The model is based on an Inception ResNet v2 architecture pooled with a Deep Adagrad (Adaptive Gradient Descent Algorithm) Long Short-Term Memory (LSTM) network. EEG signal features are extracted using the Inception and ResNet layers, and significant features are then trained with a deep convolutional neural network (CNN) integrated with an Adagrad-optimized LSTM layer to classify focal and nonfocal EEG signals. The results demonstrate that the model achieves an impressive 99.76% accuracy in automatically detecting epilepsy abnormalities.","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2025 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2/7543401","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144647303","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 Denoising Diffusion Probabilistic Model-Based Human Respiration Monitoring Method Using a UWB Radar 基于去噪扩散概率模型的超宽带雷达人体呼吸监测方法

IF 1.4 4区工程技术

IET Signal Processing Pub Date : 2025-07-11 DOI: 10.1049/sil2/1548873

Ping Wang, Haoran Liu, Xiusheng Liang, Zhenya Zhang

{"title":"A Denoising Diffusion Probabilistic Model-Based Human Respiration Monitoring Method Using a UWB Radar","authors":"Ping Wang, Haoran Liu, Xiusheng Liang, Zhenya Zhang","doi":"10.1049/sil2/1548873","DOIUrl":"10.1049/sil2/1548873","url":null,"abstract":"Real-time respiratory monitoring when sleeping is crucial for sleep apnea, chronic obstructive pulmonary disease, sleep quality assessment, and other issues related to the tracking of human health status. With the advantages of easy deployment, no wearing burden, and low privacy disclosure, recent years have witnessed a growing interest in device-free respiration monitoring leveraging radio-frequency (RF) sensing. This paper proposes a denoising diffusion probabilistic model (DDPM)-based human respiration monitoring method using an ultra-wideband (UWB) radar, where the localization calculation of the target based on the respiration-motion energy ratio, maximum ratio combining (MRC), and principal component analysis (PCA) are included for data enhancement. Moreover, a real-time sleep respiration monitoring system has been designed and implemented, which is composed of a civilian UWB radar development board, a Raspberry Pi 3B, and a PC, and extensive experiments have been carried out to validate our proposed method. Compared to the commercial respiratory tapes, our method shows that the respiratory rate estimation accuracy and the cosine similarity of respiratory waveforms can reach up to 94% and 87.9%, respectively, rendering it can be considered a viable solution for contact-free respiration monitoring for health.","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2025 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2/1548873","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598522","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

Optimizing In-Bed Posture Classification Using Tanh-Activated Kolmogorov–Arnold Networks (Tanh-KAN) 基于tanh激活Kolmogorov-Arnold网络（Tanh-KAN）的床上姿势分类优化

IF 1.4 4区工程技术

IET Signal Processing Pub Date : 2025-06-26 DOI: 10.1049/sil2/6740194

Weiwei Chen, Bing Zhou, Wai Yie Leong

{"title":"Optimizing In-Bed Posture Classification Using Tanh-Activated Kolmogorov–Arnold Networks (Tanh-KAN)","authors":"Weiwei Chen, Bing Zhou, Wai Yie Leong","doi":"10.1049/sil2/6740194","DOIUrl":"10.1049/sil2/6740194","url":null,"abstract":"In-bed posture classification plays a crucial role in health monitoring. However, existing research on classification involves a limited range of in-bed postures. Meanwhile, in classification tasks, Kolmogorov–Arnold networks (KANs), as an emerging neural network architecture, have research gaps in two areas: training strategies and architecture design. In our research, we propose Tanh-KAN, an efficient variant of KAN for in-bed posture classification. First, we validate that disabling the spline scaler not only preserves classification accuracy on the PoPu, Pmat, and SPN datasets, but also contributes to a reduction in model parameters and an increase in throughput. Second, we simplified the cubic B-spline basis functions in the original KAN using a Tanh-kernel. Compared to the original KAN, the accuracy remained stable, while the parameters were reduced by approximately 9% and the backpropagation and inference speeds increased by 42.3% and 53.9%, respectively. Experimental results further demonstrate that Tanh-KAN not only reduces model complexity and accelerates computation but also maintains high accuracy, achieving 99.6% on PoPu, 98.5% on Pmat, and 61.5% on SPN, matching the original KAN’s performance.","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2025 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2/6740194","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144482070","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

Weak Preprocessing Iris Feature Matching Based on Bipartite Graph 基于二部图的弱预处理虹膜特征匹配

IF 1.4 4区工程技术

IET Signal Processing Pub Date : 2025-06-26 DOI: 10.1049/sil2/2013549

Jin Zhang, Kangwei Wang, Rongrong Shi, Feng Xie, Qinghe Zheng, Ruizhe Zhang, Cheng Wu, Yiming Wang

{"title":"Weak Preprocessing Iris Feature Matching Based on Bipartite Graph","authors":"Jin Zhang, Kangwei Wang, Rongrong Shi, Feng Xie, Qinghe Zheng, Ruizhe Zhang, Cheng Wu, Yiming Wang","doi":"10.1049/sil2/2013549","DOIUrl":"10.1049/sil2/2013549","url":null,"abstract":"Iris recognition is widely regarded as one of the most reliable biometric identification technologies. Traditional methods, such as the Daugman algorithm typically normalize the annular iris region into a rectangular format during the preprocessing stage, followed by feature extraction and matching. However, these preprocessing steps often introduce distortions and struggle to adapt to multiresolution images, leading to inaccurate feature encoding. In response to these limitations, we propose a weak preprocessing algorithm for iris recognition that effectively preserves both grayscale and structural information of the iris. This approach is highly adaptable to varying image resolutions by leveraging a multiscale structural information extraction framework. It demonstrates significant improvements, achieving a matching accuracy of 96.67% on our proprietary dataset and 90% on the CASIA-IrisV4 dataset. Compared to the Daugman and OsIris 4.0 algorithm using weak preprocessing schemes, our approach improves accuracy by 15.55% and reduces matching time by 16%. More importantly, this method presents a new idea that is different from traditional preprocessing methods with wider adaptability. It offers considerable potential for real-world applications in security, with promising prospects for further integration with deep learning techniques.","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2025 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2/2013549","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492934","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