Signal Processing最新文献_第6页

Spectrally compatible waveform design with low correlation sidelobe for MIMO radar under time-varying spectral environment 时变频谱环境下MIMO雷达低相关旁瓣频谱兼容波形设计

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-05-15 DOI: 10.1016/j.sigpro.2025.110109

Zhaobo Jia , Lei Yu , Yinsheng Wei

{"title":"Spectrally compatible waveform design with low correlation sidelobe for MIMO radar under time-varying spectral environment","authors":"Zhaobo Jia , Lei Yu , Yinsheng Wei","doi":"10.1016/j.sigpro.2025.110109","DOIUrl":"10.1016/j.sigpro.2025.110109","url":null,"abstract":"<div><div>Modern radar operates in a spectral environment with intense and time-varying interference, which significantly affects the radar performance. To address this problem, we adopt the pulse group diversity pulse intra-coding waveform and propose the average autocorrelation integrated sidelobe level (AISL) to measure the comprehensive autocorrelation performance within a coherent processing interval. Furthermore, the weighted objective function of AISL and cross-integrated sidelobe level is established. Additionally, the spectral and constant modulus constraints are utilized to formulate the optimization problem. To solve this NP-hard problem, we transform the original problem into several easy-to-solve sub-problems based on the alternating direction method of multipliers framework. Then we use the conjugate gradient method to solve the sub-problems. We also provide a weighted value selection approach tailored to different radar performance requirements. Simulation experiments are provided to demonstrate that the proposed algorithm can accurately select appropriate weighted values under diverse conditions. Moreover, the proposed algorithm outperforms the existing algorithms in terms of sidelobe performance and execution efficiency.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110109"},"PeriodicalIF":3.4,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147342","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

SSPWave: Integrated signal subspace projection wavelet-inspired network for HRRP denoising and recognition 基于集成信号子空间投影小波的HRRP去噪与识别

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-05-15 DOI: 10.1016/j.sigpro.2025.110110

Ting Chen, Shuai Guo, Penghui Wang, Yinghua Wang, Junkun Yan, Hongwei Liu

{"title":"SSPWave: Integrated signal subspace projection wavelet-inspired network for HRRP denoising and recognition","authors":"Ting Chen, Shuai Guo, Penghui Wang, Yinghua Wang, Junkun Yan, Hongwei Liu","doi":"10.1016/j.sigpro.2025.110110","DOIUrl":"10.1016/j.sigpro.2025.110110","url":null,"abstract":"<div><div>Identifying non-cooperative targets based on HRRP is a critical and challenging task. To enhance HRRP recognition performance in harsh environments characterized by low signal-to-noise ratios (SNR), we innovatively proposed a noise-robust model that combines domain knowledge and time-frequency multi-resolution analysis, namely integrated signal subspace projection wavelet-inspired network (SSPWave). It cascades a fine-grained deep denoising model and a general recognition model. First, we attempt to integrate discrete wavelet transform (DWT) into the deep denoising model, systematically removing the high-frequency components corresponding to the noise layer by layer, while retaining the low-frequency components containing the main structure of the target on down-sampling process. Second, to reconstruct the high-frequency details required during up-sampling, we propose a signal subspace projection (SSP) module. Notably, SSP introduces the estimated SNR as prior, and facilitates waveform preservation through adaptive subspace projection. SSPWave achieves a balance between noise suppression and detail preservation with SNR-guided, demonstrating the flexibility and effectiveness in addressing various noise levels of HRRPs. We evaluated the model on two measured HRRP datasets, which exhibited advanced recognition robustness on several evaluation metrics. Most importantly, domain knowledge assistance and time-frequency multi-resolution analysis are validated as effective strategies for HRRP denoising and recognition tasks.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110110"},"PeriodicalIF":3.4,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131059","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

WaveGRU-Net: Robust non-contact ECG reconstruction via MIMO millimeter-wave radar and multi-scale semantic analysis WaveGRU-Net：基于MIMO毫米波雷达和多尺度语义分析的鲁棒非接触心电重建

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-05-15 DOI: 10.1016/j.sigpro.2025.110108

Dan Xu , Yiming Xu , Kaijie Xu , Ze Hu , Mengdao Xing , Fulvio Gini , Maria Sabrina Greco

{"title":"WaveGRU-Net: Robust non-contact ECG reconstruction via MIMO millimeter-wave radar and multi-scale semantic analysis","authors":"Dan Xu , Yiming Xu , Kaijie Xu , Ze Hu , Mengdao Xing , Fulvio Gini , Maria Sabrina Greco","doi":"10.1016/j.sigpro.2025.110108","DOIUrl":"10.1016/j.sigpro.2025.110108","url":null,"abstract":"<div><div>With the rising demand for telemedicine, non-contact heart beating monitoring has attracted significant interest due to its non-invasive and patient-friendly attributes. However, conventional approaches are typically limited to detecting the peaks of the Electrocardiogram (ECG), making the accurate extraction of ECG intervals challenging. This paper proposed a novel method for non-contact ECG signal reconstruction utilizing multiple-input-multiple-output millimeter-wave radar, enabling precise reconstruction of comprehensive ECG features and capturing nuanced variations in cardiac activity. First, Two-Dimensional beamforming is employed to enhance the radar signal of interest. The echo inevitably contains interference from random body movements and chest displacements caused by respiration. The interference from random body movements can be effectively suppressed by using a cumulative energy spectrum analysis. Next, the phase information representing the combined respiratory and cardiac micro-movements is extracted. Then, the phase is inputted into the WaveGRU-Net model, which is an advanced neural network based on the Convolutional Neural Network-Long Short-Term Memory architecture, to reconstruct heartbeat signals and ECG waveforms. The proposed method successfully separates respiratory and cardiac signals in the time-frequency domain, yielding a refined ECG reconstruction enriched with detailed semantic features that encapsulate subtle cardiac dynamics. Experimental results demonstrate the proposed method has strong semantic representation capabilities.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"237 ","pages":"Article 110108"},"PeriodicalIF":3.4,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144099520","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

OMLK-Net: An Online Multi-scale Large Separable Kernel Distillation Network for efficient image super-resolution OMLK-Net：一种用于图像超分辨率的在线多尺度大可分离核蒸馏网络

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-05-14 DOI: 10.1016/j.sigpro.2025.110078

Hanjia Wei, Weiwei Wang, Xixi Jia, Xiangchu Feng, Chuan Chen

{"title":"OMLK-Net: An Online Multi-scale Large Separable Kernel Distillation Network for efficient image super-resolution","authors":"Hanjia Wei, Weiwei Wang, Xixi Jia, Xiangchu Feng, Chuan Chen","doi":"10.1016/j.sigpro.2025.110078","DOIUrl":"10.1016/j.sigpro.2025.110078","url":null,"abstract":"<div><div>Single-image super-resolution (SISR) has seen remarkable progress in recent years, driven by the powerful learning capabilities of large-scale neural networks, such as deep CNNs and Transformers. However, these advances come at the expense of substantial computational costs. Striking a delicate balance between effectiveness and efficiency remains a key challenge in neural network design. This paper proposes OMLK-Net, a novel lightweight architecture for SISR, offering the dual advantages of computational efficiency and high effectiveness. OMLK-Net adopts a divide-and-conquer strategy to separately optimize local and nonlocal feature learning, enabling a lightweight architecture without compromising feature representation effectiveness. Specifically, our OMLK-Net comprises two key modules: an Online Multiscale Distillation Block (OMDB) and Large Separable Shifting Kernel Attention (L2SKA) blocks. The OMDB module aims to explore multiscale local contextual information with a customized lightweight network block; while the L2SKA aims to harness nonlocal features by using computationally efficient large separable shifting kernels. By virtue of its carefully designed local and nonlocal feature extraction operators, OMLK-Net effectively addresses SISR challenges while maintaining low computational complexity. Extensive experimental results on benchmark datasets demonstrate that OMLK-Net achieves a better trade-off against state-of-the-art methods in terms of performance and model complexity. Codes will be available soon.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"237 ","pages":"Article 110078"},"PeriodicalIF":3.4,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069101","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

Imputation of time-varying edge flows in graphs by multilinear kernel regression and manifold learning 基于多线性核回归和流形学习的时变边流图的插值

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-05-10 DOI: 10.1016/j.sigpro.2025.110077

Duc Thien Nguyen , Konstantinos Slavakis , Dimitris Pados

引用次数: 0

Underdetermined DOA estimation of quasi-stationary signals via virtual array interpolation 基于虚阵插值的准平稳信号欠定DOA估计

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-05-10 DOI: 10.1016/j.sigpro.2025.110076

Kangning Li , Qing Shen , Wei Liu , Zexiang Zhang , Tianyuan Gu , Wei Cui

{"title":"Underdetermined DOA estimation of quasi-stationary signals via virtual array interpolation","authors":"Kangning Li , Qing Shen , Wei Liu , Zexiang Zhang , Tianyuan Gu , Wei Cui","doi":"10.1016/j.sigpro.2025.110076","DOIUrl":"10.1016/j.sigpro.2025.110076","url":null,"abstract":"<div><div>An underdetermined direction of arrival (DOA) estimation method for quasi-stationary signals (QSSs) using virtual array interpolation is proposed. A second-order difference co-array model based on quasi-stationary signals is first constructed. This model is then interpolated into a uniform linear array (ULA). Instead of processing each time frame individually, a single matrix completion operation is applied across all time frames simultaneously. This method leverages the quasi-stationarity of the signals and the low-rank property of the auto-covariance matrix for matrix completion. An alternating direction method of multipliers (ADMM) based solution is introduced to solve the matrix completion problem, which is more efficient than the commonly used semi-definite programming (SDP) framework. Subsequently, the subspace method is utilized on the completed covariance matrix for DOA estimation. Comparative analysis with the existing interpolation-based QSS DOA estimation method demonstrates that the proposed method achieves superior accuracy and efficiency.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"237 ","pages":"Article 110076"},"PeriodicalIF":3.4,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935685","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

Domain-aware Gaussian process state-space models 域感知高斯过程状态空间模型

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-05-10 DOI: 10.1016/j.sigpro.2025.110003

Anurodh Mishra, Raj Thilak Rajan

{"title":"Domain-aware Gaussian process state-space models","authors":"Anurodh Mishra, Raj Thilak Rajan","doi":"10.1016/j.sigpro.2025.110003","DOIUrl":"10.1016/j.sigpro.2025.110003","url":null,"abstract":"<div><div>Gaussian process state-space models are a widely used modeling paradigm for learning and estimation in dynamical systems. Reduced-rank Gaussian process state-space models combine spectral characterization of dynamical systems with Hilbert space methods to enable learning, which scale linearly with the length of the time series. However, the current state of the art algorithms struggle to deal efficiently with the dimensionality of the state-space itself. In this work, we propose a novel algorithm, referred to as Domain-Aware reduced-rank Gaussian Process State-Space Model (DA-GPSSM), which exploits the relationship between state dimensions to model only necessary dynamics resulting in reduced computational cost, by potentially orders of magnitude in comparison to the state-of-the-art. The proposed approach grants modeling flexibility while maintaining comparable performance and thus increasing the applicability of these models. We present implications of the proposed approach and discuss applications where DA-GPSSM can be beneficial. Finally, we conduct simulations to demonstrate the performance and reduced computational cost of our proposed method, compared to the state-of-the-art learning method, and propose future research directions.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110003"},"PeriodicalIF":3.4,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147344","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 frequency estimation through iterative parabolic interpolations 精确的频率估计通过迭代抛物线插值

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-05-08 DOI: 10.1016/j.sigpro.2025.110094

Andrea Togni , Marco Zannoni , Paolo Tortora

{"title":"Accurate frequency estimation through iterative parabolic interpolations","authors":"Andrea Togni , Marco Zannoni , Paolo Tortora","doi":"10.1016/j.sigpro.2025.110094","DOIUrl":"10.1016/j.sigpro.2025.110094","url":null,"abstract":"<div><div>Extracting the frequency from a complex signal is a common task in many applications, and multiple methods exist for accurate frequency estimation under different noise conditions. Iterative algorithms based on the interpolation of the discrete Fourier transform (DFT) are known to achieve high accuracy, and methods that employ auxiliary coefficients around the peak of the DFT are recurrent in the literature. This paper presents a novel iterative algorithm for frequency estimation based on successive parabolic interpolations of three DFT coefficients. Unlike other similar methods, which typically require auxiliary fine estimators for bias reduction, the proposed method refines the frequency estimate by progressively decreasing the offset of the DFT coefficients employed at each iteration. This approach eliminates the need for external correction steps and enhances estimation accuracy as the interpolation narrows around the true frequency. The algorithm achieves performance very close to the Cramér-Rao lower bound while maintaining computational efficiency, and the fine estimation step implemented can be flexibly applied to signals with or without zero-padding, making its use suitable for a wide range of signal processing applications. Simulations confirm the high accuracy and robustness to noise of the proposed estimator, showing comparable or better performance than existing iterative techniques.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"237 ","pages":"Article 110094"},"PeriodicalIF":3.4,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069212","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

A hybrid perceptron with cross-domain transferability towards active steady-state non-line-of-sight imaging 面向主动稳态非视距成像的具有跨域可转移性的混合感知器

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-05-08 DOI: 10.1016/j.sigpro.2025.110072

Rui Liang, Xi Tong, Jiangxin Yang, Yanpeng Cao

{"title":"A hybrid perceptron with cross-domain transferability towards active steady-state non-line-of-sight imaging","authors":"Rui Liang, Xi Tong, Jiangxin Yang, Yanpeng Cao","doi":"10.1016/j.sigpro.2025.110072","DOIUrl":"10.1016/j.sigpro.2025.110072","url":null,"abstract":"<div><div>Active steady-state non-line-of-sight (NLOS) imaging entails the acquisition and processing of continuous multi-bounce NLOS signals to facilitate the recovery of hidden scenes. Recently, learning-based methods have demonstrated competitive performance in NLOS imaging. However, most of them inadequately capture the underlying features inherent in acquired signals and fail to effectively exploit prior information from hidden scenes, thus constraining their ability to alleviate the ill-posedness. To address the above limitations, we propose a novel NLOS signal processing framework—hybrid perceptron with cross-domain transferability (HP-CDT). The HP enhances the utilization of primitive features through a hierarchical pooling and feature fusion (HPFI) mechanism while comprehensively capturing underlying correlations within the signals via local and global perception. Besides, it facilitates cross-level interactions and fusion of various features, thereby enriching the feature representation. The cross-domain transfer (CDT) strategy leverages line-of-sight (LOS) latent representations as priors to steer the NLOS feature extraction, facilitating the optimization of NLOS latent representations. Rendering experiments and practical assessment indicate that, compared with existing methods, our approach achieves superior imaging quality while maintaining a light-weight architecture for efficient deployment.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"237 ","pages":"Article 110072"},"PeriodicalIF":3.4,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931641","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

A scalable Consensus Fast Graph Filtering approach for late fusion multi-view clustering 面向后期融合多视图聚类的可扩展共识快速图滤波方法

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-05-07 DOI: 10.1016/j.sigpro.2025.110074

Yiqing Guo , Henghui Jiang , Yan Chen , Liang Du

{"title":"A scalable Consensus Fast Graph Filtering approach for late fusion multi-view clustering","authors":"Yiqing Guo , Henghui Jiang , Yan Chen , Liang Du","doi":"10.1016/j.sigpro.2025.110074","DOIUrl":"10.1016/j.sigpro.2025.110074","url":null,"abstract":"<div><div>The rapid growth of multi-view data presents significant challenges for clustering algorithms due to its complexity and high dimensionality. Late fusion multi-view clustering (LFMVC) often suffer from low-quality base partitions. To address these challenges, we propose a scalable method called Consensus Fast Graph Filtering for late fusion (CFGFLF) multi-view clustering. This approach integrates multi-view consensus graph filtering with discrete clustering into a unified optimization framework, enhancing clustering accuracy and keeping efficiency. CFGFLF constructs bipartite graphs for each view to capture local relationships, applies higher-order graph diffusion to model global relationships, and refines base partitions through the low-pass filtering property of graph filters. By avoiding costly operations like matrix inversions and utilizing low-rank bipartite graph structures, CFGFLF achieves linear complexity for base partition filtering. Experimental results show that CFGFLF outperforms state-of-the-art methods in clustering accuracy, particularly for large-scale datasets and noisy environments, without sacrificing computational efficiency. The codes of this paper are released in <span><span>https://github.com/GuoYiqing1/CFGFLF</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"237 ","pages":"Article 110074"},"PeriodicalIF":3.4,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928711","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