Signal Processing最新文献_第10页

Image-set classification using Discriminant Neighborhood Preserving Embedding on Grassmann manifold 格拉斯曼流形上判别邻域保持嵌入的图像集分类

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-04-12 DOI: 10.1016/j.sigpro.2025.110028

Benchao Li , Yuanyuan Zheng , Ruisheng Ran , Bin Fang

引用次数: 0

Power spectrum in the free metaplectic transformation domain: Theory and application 自由元折中变换域中的功率谱：理论与应用

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-04-11 DOI: 10.1016/j.sigpro.2025.110039

Changjie Sheng , Sen Shi , Zhichao Zhang

{"title":"Power spectrum in the free metaplectic transformation domain: Theory and application","authors":"Changjie Sheng , Sen Shi , Zhichao Zhang","doi":"10.1016/j.sigpro.2025.110039","DOIUrl":"10.1016/j.sigpro.2025.110039","url":null,"abstract":"<div><div>The parameter estimation of chirp signals has emerged as a prominent topic in the signal processing domain. Although existing estimation methods can accurately determine signal parameters, they are often ineffective for chirp signals characterized by unseparable terms. This study introduces the concept of the free metaplectic power spectrum (FMPS) and free metaplectic correlation function (FMCF) for random processes, and derives the relationship between the FMPS for the input and output of free metaplectic transform domain filters. Additionally, the interrelationship between the FMCF and FMPS is explored. The simulation utilizes derived theories alongside a coarse-to-fine search strategy for parameter estimation. The results indicate that the FMPS method significantly outperforms traditional techniques in estimating parameters of chirp signals with unseparable terms, while it maintains estimation accuracy comparable to the conventional power spectrum methods for chirp signals devoid of unseparable terms. At the end of the paper, some potential applications of the proposed method in fields of radar and communications are described.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"235 ","pages":"Article 110039"},"PeriodicalIF":3.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833438","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

Dual-function waveform optimization algorithm of joint transmitter and receiver via W-ADPM 通过 W-ADPM 实现联合发射机和接收机的双功能波形优化算法

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-04-11 DOI: 10.1016/j.sigpro.2025.110011

Wenshuai Ji, Hangyu Lin, Tao Liu

{"title":"Dual-function waveform optimization algorithm of joint transmitter and receiver via W-ADPM","authors":"Wenshuai Ji, Hangyu Lin, Tao Liu","doi":"10.1016/j.sigpro.2025.110011","DOIUrl":"10.1016/j.sigpro.2025.110011","url":null,"abstract":"<div><div>This paper proposes a novel algorithm for joint transmitter and receiver dual-function radar communication (DFRC) systems based on orthogonal frequency division multiplexing (OFDM) waveforms. The algorithm achieves a better detection performance while containing communication bit error rate (BER) performance, modulating communication information into the phase of the OFDM waveform using M-phase-shift keying (MPSK) schemes such as Binary-PSK (BPSK) and Qinary-PSK (QPSK). Subsequently, the waveform minimizes the weighted peak sidelobe level (WPSL) of the transmit waveform and the receive mismatch filter while ensuring the bit error rate (BER) condition to reduce sidelobes. Additionally, constraints are placed on constant amplitude, BER, mainlobe energy, and signal-to-noise ratio (SNR) loss. This paper employs a Weight Alternating Direction Method of Penalty (W-ADPM) network-based approach to simultaneously optimize the transmit waveform and receive mismatched filters to address these issues, achieving the desired effect. The simulation experiments demonstrate that the proposed algorithm has better convergence performance for the DFRC OFDM waveform compared to the Alternating Direction Method of Multipliers (ADMM) algorithm. Besides, the simulation experiments show that, compared to traditional matched filters, the jointly transmitted and received mismatched filters proposed in this paper provide better ISL cross-correlation performance while ensuring the BER.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"235 ","pages":"Article 110011"},"PeriodicalIF":3.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833436","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

Enhancing jamming source tracking capability via adaptive grey wolf optimization mechanism for passive radar network 利用自适应灰狼优化机制增强无源雷达网络干扰源跟踪能力

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-04-11 DOI: 10.1016/j.sigpro.2025.110026

Wen Jiang, Zhen Liu, Yanping Wang, Yun Lin, Yang Li, Fukun Bi

{"title":"Enhancing jamming source tracking capability via adaptive grey wolf optimization mechanism for passive radar network","authors":"Wen Jiang, Zhen Liu, Yanping Wang, Yun Lin, Yang Li, Fukun Bi","doi":"10.1016/j.sigpro.2025.110026","DOIUrl":"10.1016/j.sigpro.2025.110026","url":null,"abstract":"<div><div>In a complex electromagnetic environment, the tracking of jamming source by passive radar network is of great significance for enhancing anti-jamming capability, military combat safety, and strategic decision-making. However, traditional jamming source tracking algorithms suffer from low tracking accuracy and convergence speed, primarily due to the high nonlinearity and the unknown noise characteristics of the passive radar system. In order to improve the capability of jamming source tracking for passive radar network, a maximum correntropy cubature Kalman filter based on improved grey wolf optimization algorithm is proposed. Firstly, the grey wolf optimization mechanism improved by Gaussian random walk and Gaussian mutation strategies is proposed to accurately estimate the characteristics of unknown process and measurement noise, providing more accurate model parameters for the cubature Kalman filter algorithm. Then, an adaptive maximum correntropy criterion is designed, which optimizes the filter gain by adaptively adjusting the kernel size to suppress the influence of outliers on the filtering estimation and enhances the robustness of the algorithm. Finally, experiment of jamming source tracking indicates that the proposed algorithm significantly outperforms traditional algorithms in terms of tracking accuracy and convergence speed under diverse unknown noise environments.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"235 ","pages":"Article 110026"},"PeriodicalIF":3.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817743","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

MAGNET: Multi-level feature guidance network for few-shot fine-grained image classification MAGNET：用于少镜头细粒度图像分类的多级特征引导网络

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-04-10 DOI: 10.1016/j.sigpro.2025.110031

Penghao Jia, Guanglei Gou, Yu Cheng

{"title":"MAGNET: Multi-level feature guidance network for few-shot fine-grained image classification","authors":"Penghao Jia, Guanglei Gou, Yu Cheng","doi":"10.1016/j.sigpro.2025.110031","DOIUrl":"10.1016/j.sigpro.2025.110031","url":null,"abstract":"<div><div>Few-shot fine-grained image classification aims to distinguish highly similar categories with limited labeled samples. However, existing methods face three limitations. First, they fail to effectively feature according to the characteristics of each layer, overlooking the fine-grained structures in low-level features. Second, they handle high-level features simplistically, lacking the ability to share knowledge across multiple tasks, and they struggle with background redundancy in mid-level features, leading to overfitting. To this end, we propose a Multi-Level Feature Guidance Network (MAGNET), which integrates three core modules. The Primary Information Enhancement Module enhances low-level features by capturing fine-grained structural information and reinforcing them with high-level features. The Wavelet Attention Knowledge Guidance module applies wavelet transform for frequency-domain analysis of high-level features, while a multi-task-related knowledge transfer mechanism improves the model’s ability to share knowledge across tasks, enhancing generalization to new categories. The Background Filtering Module reduces background redundancy in mid-level features using high-level semantic information, mitigating overfitting. Extensive experiments on three benchmark datasets demonstrate that MAGNET outperforms existing methods. The source code is available at <span><span>https://github.com/naivejph/MAGNET.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"235 ","pages":"Article 110031"},"PeriodicalIF":3.4,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817741","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

Non-negative sparse signal recovery using the integration of ReLU and hard thresholding pursuit operators 基于ReLU和硬阈值追踪算子的非负稀疏信号恢复

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-04-10 DOI: 10.1016/j.sigpro.2025.110032

Pradyumna Pradhan , Sk Md Atique Anwar , Ramunaidu Randhi , Pradip Sasmal

{"title":"Non-negative sparse signal recovery using the integration of ReLU and hard thresholding pursuit operators","authors":"Pradyumna Pradhan , Sk Md Atique Anwar , Ramunaidu Randhi , Pradip Sasmal","doi":"10.1016/j.sigpro.2025.110032","DOIUrl":"10.1016/j.sigpro.2025.110032","url":null,"abstract":"<div><div>Linear inverse problems involving non-negative sparse approximations are essential in various applications such as face recognition, DNA microarrays, and spectral unmixing. Recent advancements in ReLU-based algorithms, such as ReLU-based hard thresholding (RHT) and momentum-boosted adaptive thresholding (MBAT), solve this problem by leveraging the rectified linear unit (ReLU) in combination with thresholding operators to produce non-negative sparse solutions. Despite these developments, challenges persist in achieving high recovery performance and faster convergence. To address these issues, we propose a novel ReLU-based algorithm for non-negative sparse signal recovery, termed ReLU-based hard thresholding pursuit (RHTP). Specifically, RHTP integrates the ReLU within the hard thresholding pursuit framework to enable efficient recovery of non-negative sparse signals. We derive sufficient criteria for ensuring the stable recovery of sparse signals generated from RHTP based on the restricted isometry property. Additionally, we provide a theoretical analysis showing that the RHTP algorithm converges more rapidly than the RHT algorithm. Numerical experiments demonstrate that RHTP outperforms existing algorithms in recovering binary sparse signals and delivers comparable performance to the state-of-the-art MBAT algorithm in recovering non-negative sparse Gaussian signals. Furthermore, empirical results demonstrate that RHTP exhibits faster convergence compared to other methods. Moreover, RHTP achieves higher classification accuracy than other non-negative sparse signal recovery algorithms on the Yale Face dataset, demonstrating its effectiveness in face recognition.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"236 ","pages":"Article 110032"},"PeriodicalIF":3.4,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848416","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

Low-resolution compressed sensing and beyond for communications and sensing: Trends and opportunities 用于通信和传感的低分辨率压缩传感及其他：趋势和机遇

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-04-10 DOI: 10.1016/j.sigpro.2025.110020

Geethu Joseph , Venkata Gandikota , Ayush Bhandari , Junil Choi , In-soo Kim , Gyoseung Lee , Michail Matthaiou , Chandra R. Murthy , Hien Quoc Ngo , Pramod K. Varshney , Thakshila Wimalajeewa , Wei Yi , Ye Yuan , Guoxin Zhang

{"title":"Low-resolution compressed sensing and beyond for communications and sensing: Trends and opportunities","authors":"Geethu Joseph , Venkata Gandikota , Ayush Bhandari , Junil Choi , In-soo Kim , Gyoseung Lee , Michail Matthaiou , Chandra R. Murthy , Hien Quoc Ngo , Pramod K. Varshney , Thakshila Wimalajeewa , Wei Yi , Ye Yuan , Guoxin Zhang","doi":"10.1016/j.sigpro.2025.110020","DOIUrl":"10.1016/j.sigpro.2025.110020","url":null,"abstract":"<div><div>This survey paper examines recent advancements in low-resolution signal processing, emphasizing quantized compressed sensing. Rising costs and power demands of high-sampling-rate data acquisition drive the interest in quantized signal processing, particularly in wireless communication systems and Internet of Things sensor networks, as 6G aims to integrate sensing and communication within cost-effective hardware. Motivated by this urgency, this paper covers novel signal processing algorithms designed to address practical challenges arising from quantization and modulo operations, as well as their impact on system performance. We begin by introducing the framework of one-bit compressed sensing and discuss relevant theories and algorithms. We explore the application of quantized compressed sensing algorithms to sensor networks, radar, cognitive radio, and wireless channel estimation. We highlight how generic methods can be tailored to an application using specific examples from wireless channel estimation. Additionally, we review other low-resolution techniques beyond one-bit compressed sensing along with their applications. We also provide a brief overview of the emerging concept of unlimited sampling. While this paper does not aim to be exhaustive, it selectively highlights results to inspire readers to appreciate the diverse algorithmic tools (convex optimization, greedy methods, and Bayesian approaches) and sampling techniques (task-based quantization and unlimited sampling).</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"235 ","pages":"Article 110020"},"PeriodicalIF":3.4,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817742","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

Cubic NK-SVD: An algorithm for designing parametric dictionary in frequency estimation 立方 NK-SVD：在频率估计中设计参数字典的算法

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-04-09 DOI: 10.1016/j.sigpro.2025.110029

Xiaozhi Liu, Yong Xia

{"title":"Cubic NK-SVD: An algorithm for designing parametric dictionary in frequency estimation","authors":"Xiaozhi Liu, Yong Xia","doi":"10.1016/j.sigpro.2025.110029","DOIUrl":"10.1016/j.sigpro.2025.110029","url":null,"abstract":"<div><div>We propose a novel parametric dictionary learning algorithm for line spectral estimation, applicable in both single measurement vector (SMV) and multiple measurement vectors (MMV) scenarios. This algorithm, termed cubic Newtonized K-SVD (NK-SVD), extends the traditional K-SVD method by incorporating cubic regularization into Newton refinements. The proposed Gauss–Seidel scheme not only enhances the accuracy of frequency estimation over the continuum but also achieves better convergence by incorporating higher-order derivative information. A key contribution of this work is the rigorous convergence analysis of the proposed algorithm within the Block Coordinate Descent (BCD) framework. To the best of our knowledge, this is the first convergence analysis of BCD with a higher-order regularization scheme. Moreover, the convergence framework we develop is generalizable, providing a foundation for designing alternating minimization algorithms with higher-order regularization techniques. Extensive simulations demonstrate that cubic NK-SVD outperforms state-of-the-art methods in both SMV and MMV settings, particularly excelling in the challenging task of recovering closely-spaced frequencies. The code for our method is available at <span><span>https://github.com/xzliu-opt/Cubic-NK-SVD</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"235 ","pages":"Article 110029"},"PeriodicalIF":3.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817740","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

Double-optimized symmetric geometric mode decomposition with dispersion entropy and its application in feature extraction 色散熵双优化对称几何模态分解及其在特征提取中的应用

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-04-09 DOI: 10.1016/j.sigpro.2025.110046

Yuxing Li , Xuanming Cheng

{"title":"Double-optimized symmetric geometric mode decomposition with dispersion entropy and its application in feature extraction","authors":"Yuxing Li , Xuanming Cheng","doi":"10.1016/j.sigpro.2025.110046","DOIUrl":"10.1016/j.sigpro.2025.110046","url":null,"abstract":"<div><div>Symmetric geometric mode decomposition (SGMD) offers notable advantages in preserving the basic features of time series and in noise robustness. However, SGMD faces issues related to inaccurate mode decomposition and parameter selection. To address these problems, this paper proposes a double-optimized symmetric geometric mode decomposition with dispersion entropy (DSGMDDE). This algorithm incorporates dispersion entropy(DisE) as an indicator for mode reconstruction, enhancing the accuracy of mode decomposition. Furthermore, a double optimization algorithm is introduced to optimize parameters, thereby improving the effectiveness of the algorithm. By combining DSGMDDE with DisE, a feature extraction method named DSGMDDE-DisE is proposed. Simulation results demonstrate that, compared to four other mode decomposition algorithms, DSGMDDE offers higher decomposition accuracy and better robustness. Furthermore, DSGMDDE-DisE shows superior feature extraction capability compared to the other four feature extraction methods. Real-world experiment results indicate that DSGMDDE-DisE can more accurately distinguish between eight types of ship radiated noises (SRNs) and five types of Southeast University bearings (SUBs) fault signals.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"235 ","pages":"Article 110046"},"PeriodicalIF":3.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829834","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

Irregular time-varying series prediction on graphs with nonlinear expansion functions 非线性展开函数图的不规则时变序列预测

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-04-09 DOI: 10.1016/j.sigpro.2025.110025

Wenjuan Li , Ming Jin , Junzheng Jiang , Qinghua Guo , Wanyuan Cai

{"title":"Irregular time-varying series prediction on graphs with nonlinear expansion functions","authors":"Wenjuan Li , Ming Jin , Junzheng Jiang , Qinghua Guo , Wanyuan Cai","doi":"10.1016/j.sigpro.2025.110025","DOIUrl":"10.1016/j.sigpro.2025.110025","url":null,"abstract":"<div><div>Predicting irregular time-varying series is challenging due to the complex interdependencies among variables. To capture the nonlinear spatiotemporal relationships in the data evolution process, we propose two nonlinear prediction methods that incorporate nonlinear expansion functions and graph signal processing (GSP). First, we develop a nonlinear graph vector autoregressive (NL-GVAR) model equipped with a nonlinear expansion module. This model maps graph signals from low-dimensional to high-dimensional spaces to enhance the nonlinear representation capability. Second, to address the impact of fluctuations in non-stationary time series, we integrate empirical mode decomposition (EMD) into the NL-GVAR framework. This integration allows for the efficient capture of the underlying nonlinear interdependencies within the time series. Furthermore, we derive closed-form solutions for parameter optimization under the minimum mean square error (MSE) criterion. Numerical results using various synthetic and real-world datasets demonstrate the superior performance of the proposed methods compared to existing methods.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"235 ","pages":"Article 110025"},"PeriodicalIF":3.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808788","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