IEEE open journal of signal processing最新文献

Robust Kalman Filtering via Correntropy-Based Higher-Order Moment Adaptation and Variable Structure Gains 基于相关熵的高阶矩自适应和变结构增益鲁棒卡尔曼滤波

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-02-23 DOI: 10.1109/OJSP.2026.3667079

Waleed Hilal;Alex McCafferty-Leroux;John Yawney;S. Andrew Gadsden

{"title":"Robust Kalman Filtering via Correntropy-Based Higher-Order Moment Adaptation and Variable Structure Gains","authors":"Waleed Hilal;Alex McCafferty-Leroux;John Yawney;S. Andrew Gadsden","doi":"10.1109/OJSP.2026.3667079","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3667079","url":null,"abstract":"This paper proposes two novel filtering strategies as sub-optimal robust solutions for state estimation in systems affected by non-Gaussian noise, outliers, or modeling uncertainties. The moments-based Kalman filter (MKF) and moments-based innovation filter (MIF) replace the mean squared error criterion with a correntropy-based cost function that incorporates higher-order statistical moments of the innovation sequence. Through Taylor series expansion of the Gaussian kernel, correntropy inherently captures all even-order moments—including variance, kurtosis, and higher-order statistics—providing natural robustness to heavy-tailed and asymmetric noise distributions. An adaptive kernel bandwidth mechanism uses real-time estimates of innovation skewness and kurtosis to automatically balance efficiency and robustness. The MIF augments this framework with variable structure control theory, incorporating a saturation-based gain that bounds corrective action during large disturbances. Both methods employ fixed-point iteration with correntropy-weighted covariance matrices in their predictor-corrector algorithms. Mathematical derivations and stability proofs are provided for both filters. The approaches extend to nonlinear systems through first-order Taylor series linearization, yielding the extended MKF (EMKF) and extended MIF (EMIF). To validate their robustness relative to the conventional Kalman filter, the proposed methods are applied to both linear and nonlinear representations of a simulated electrohydrostatic actuator (EHA) experiencing leakage faults. Computational experiments demonstrate that the MKF and MIF achieve superior estimation accuracy compared to the KF under non-Gaussian conditions, more faithfully representing faulty system behavior.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"343-355"},"PeriodicalIF":2.7,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11406897","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diverse Subset Selection via Norm-Based Sampling and Orthogonality 基于范数采样和正交性的多样化子集选择

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-02-20 DOI: 10.1109/OJSP.2026.3666822

Noga Bar;Raja Giryes

引用次数: 0

Robust Localization in Modern Cellular Networks Using Global Map Features 基于全局地图特征的现代蜂窝网络鲁棒定位

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-02-16 DOI: 10.1109/OJSP.2026.3665385

Junshi Chen;Xuhong Li;Russ Whiton;Erik Leitinger;Fredrik Tufvesson

{"title":"Robust Localization in Modern Cellular Networks Using Global Map Features","authors":"Junshi Chen;Xuhong Li;Russ Whiton;Erik Leitinger;Fredrik Tufvesson","doi":"10.1109/OJSP.2026.3665385","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3665385","url":null,"abstract":"Radio frequency (RF) signal-based localization using modern cellular networks has emerged as a promising solution to accurately locate objects in challenging environments. One of the most promising solutions for situations involving obstructed-line-of-sight (OLoS) and multipath propagation is multipath-based simultaneous localization and mapping (MP-SLAM) that employs map features (MFs), such as virtual anchors. This paper presents an extended MP-SLAM method that is augmented with a global map feature (GMF) repository. This repository stores consistent MFs of high quality that are collected during prior traversals. We integrate these GMFs back into the MP-SLAM framework via a probability hypothesis density (PHD) filter, which propagates GMF intensity functions over time. Extensive simulations, together with a challenging real-world experiment using LTE RF signals in a dense urban scenario with severe multipath propagation and inter-cell interference, demonstrate that our framework achieves robust and accurate localization, thereby showcasing its effectiveness in realistic modern cellular networks such as 5G or future 6G networks. It outperforms conventional proprioceptive sensor-based localization and conventional MP-SLAM methods, and achieves reliable localization even under adverse signal conditions.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"356-372"},"PeriodicalIF":2.7,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11397371","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sampling Method for Generalized Graph Signals With Pre-Selected Vertices via DC Optimization 基于DC优化的带预选点的广义图信号采样方法

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-02-13 DOI: 10.1109/OJSP.2026.3664335

Keitaro Yamashita;Kazuki Naganuma;Shunsuke Ono

{"title":"Sampling Method for Generalized Graph Signals With Pre-Selected Vertices via DC Optimization","authors":"Keitaro Yamashita;Kazuki Naganuma;Shunsuke Ono","doi":"10.1109/OJSP.2026.3664335","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3664335","url":null,"abstract":"This paper proposes a method for vertex-wise aggregation sampling of a broad class of graph signals, designed to attain the best possible recovery based on the generalized sampling theory. This is achieved by designing a sampling operator by an optimization problem, which is inherently non-convex, as the best possible recovery imposes a rank constraint. An existing method for vertex-wise aggregation sampling is able to control the number of active vertices but cannot incorporate prior knowledge of mandatory or avoided vertices. To address these challenges, we formulate the operator design as a problem that handles a constraint on the number of active vertices and prior knowledge on specific vertices for sampling, mandatory inclusion or exclusion. We transformed this constrained problem into a difference-of-convex (DC) optimization problem by using the nuclear norm and a DC penalty for vertex selection. To solve this, we develop a convergent solver based on the general double-proximal gradient DC algorithm. The effectiveness of our method is demonstrated through experiments on various graph signal models, including real-world data, showing superior performance in the recovery accuracy compared to existing methods.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"314-323"},"PeriodicalIF":2.7,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11395613","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Alternatives to Sine Carrier in Auditory BCI: Exploring Machine Learning Strategies for Assessing Modulation Detectability in EEG 听觉脑机接口中正弦载波的替代方案：探索脑电调制可检测性评估的机器学习策略

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-02-13 DOI: 10.1109/OJSP.2026.3664271

Lenaïg Guého;Henrique Lefundes da Silva;Cyril Plapous;Laurent Bougrain;Patrick Hénaff;Rozenn Nicol

{"title":"Alternatives to Sine Carrier in Auditory BCI: Exploring Machine Learning Strategies for Assessing Modulation Detectability in EEG","authors":"Lenaïg Guého;Henrique Lefundes da Silva;Cyril Plapous;Laurent Bougrain;Patrick Hénaff;Rozenn Nicol","doi":"10.1109/OJSP.2026.3664271","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3664271","url":null,"abstract":"In this paper, the use of non-sinusoidal amplitude-modulated stimuli is assessed for Brain-Computer Interfaces (BCIs) based on Steady-State Auditory Evoked Potentials (SSAEPs). Three different stimuli are compared to the frequently used 1-kHz pure tone: Brownian noise, cicada song and cat's purr. While these alternative sounds are intended to be more pleasant for listeners, they may impact the detectability of the modulation frequency in ElectroEncephaloGraphic (EEG) signals. Stimuli are equalized in loudness using an Head And Torso Simulator (HATS). The experiment is conducted at two loudness levels (50 and 56 phons), with 24 subjects participating in each condition. Hearing capacity is assessed prior to the experiment, using an audiometry test and questionnaires. For each stimulus, detection is performed by using 10 different classifiers: a linear discriminant analysis, deep learning networks and Riemannian classifiers including tangent space-based algorithms. These latter consistently outperformed alternative approaches. Pure tones provide the highest accuracy of detection (above 83%), whereas cicada song only achieve 60%. Classification using the proposed models fails for Brownian noise and cat's purr, with accuracy at a chance level. Additionally, increasing the loudness of the stimuli does not enhance the detectability of the modulation frequency for any stimulus. Amplitude modulation, frequency content and temporal characteristics of stimuli are further analyzed for explanation. These findings provide practical recommendations for auditory BCI classification and audio stimuli design.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"324-332"},"PeriodicalIF":2.7,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11395630","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EcDiff-LLIE: Event-Conditional Diffusion Model for Structure-Preserving Low-Light Image Enhancement EcDiff-LLIE：保结构弱光图像增强的事件条件扩散模型

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-02-06 DOI: 10.1109/OJSP.2026.3662627

Ramna Maqsood;Paulo Nunes;Luís Ducla Soares;Caroline Conti

{"title":"EcDiff-LLIE: Event-Conditional Diffusion Model for Structure-Preserving Low-Light Image Enhancement","authors":"Ramna Maqsood;Paulo Nunes;Luís Ducla Soares;Caroline Conti","doi":"10.1109/OJSP.2026.3662627","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3662627","url":null,"abstract":"Low-light image enhancement (LLIE) aims to restore the visual quality of poorly illuminated images by recovering fine details and textures while suppressing noise and artifacts. Recently, diffusion models have shown superior generative capabilities for LLIE. However, existing diffusion-based methods condition the denoising process only on low-light images or features derived from them (e.g., structural or illumination maps). Since the low-light images are severely degraded, this limits the denoising model’s ability to restore fine structure and reduce artifacts. In this work, we show that the event data captured simultaneously with the low-light images provides complementary high-dynamic-range and high-temporal-resolution structural information that can overcome this limitation. Therefore, we propose EcDiff-LLIE, a novel event-conditional diffusion framework for LLIE. At its core, we introduce a multimodality denoising network that conditions on both low-light images and concurrent event streams. To effectively fuse the two modalities, we design a cross-modality attention block that bridge their domain differences, while also enabling long-range dependency modeling for improved structural preservation. Experiments on the synthetic SDSD and real-world SDE datasets show significant improvements in quantitative evaluation metrics. Furthermore, evaluation on the high-resolution real-world HUE dataset further shows the generalization ability of the proposed framework.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"266-275"},"PeriodicalIF":2.7,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11373830","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VCP: Visible Context Propagation for Electrocardiogram Recovery VCP：心电图恢复的可见上下文传播

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-01-28 DOI: 10.1109/OJSP.2026.3657696

Jaeho Park;Yong-Yeon Jo;Jong-Hwan Jang;Jin Yu;Joon-myoung Kwon;Junho Song

{"title":"VCP: Visible Context Propagation for Electrocardiogram Recovery","authors":"Jaeho Park;Yong-Yeon Jo;Jong-Hwan Jang;Jin Yu;Joon-myoung Kwon;Junho Song","doi":"10.1109/OJSP.2026.3657696","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3657696","url":null,"abstract":"Electrocardiograms (ECGs) remain widely archived as paper ECG charts. In the 12-lead paper ECG chart layout, each lead shows only 2.5-second visible segments. Therefore, digitized charts are incomplete, leaving most of the 10-second recording invisible and misaligned with the digital standard required by ECG-AI models. Previous work has attempted to recover these invisible segments but has shown markedly lower performance than visible segments. We propose the <bold>Visible Context Propagation (VCP)</b> architecture, an extension of ECGrecover, which leverages the quasi-periodic structure of ECGs and employs cross-attention to propagate contextual information from visible to invisible segments. Our model consistently outperformed ECGrecover, the strongest baseline, reducing RMSE by 32.4% overall, including 12.0% on invisible segments. Beyond recovery accuracy, evaluations on ECG applications demonstrated that recovered ECGs achieved performance comparable to raw ECGs in both diagnostic classification and ECG feature measurement. These results highlight the effectiveness of explicitly modeling the propagation of visible-to-invisible context and establish VCP as a robust solution for recovering incomplete paper-based ECGs, enabling reliable surrogates for clinical and analytical use.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"185-194"},"PeriodicalIF":2.7,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11363447","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-Labeling Sounds Using Optimal Transport 使用最佳传输的自标签声音

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-01-28 DOI: 10.1109/OJSP.2026.3659053

Manu Harju;Frederic Font;Annamaria Mesaros

引用次数: 0

From Informed Independent Vector Extraction to Hybrid Architectures for Target Source Extraction 从知情独立矢量提取到目标源提取的混合体系结构

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-01-26 DOI: 10.1109/OJSP.2026.3657698

Zbyněk Koldovský;Jiří Málek;Martin Vrátný;Tereza Vrbová;Jaroslav Čmejla;Stephen O'Regan

引用次数: 0

Better Naturalness Evaluation of TTS Systems TTS系统更好的自然度评价

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-01-23 DOI: 10.1109/OJSP.2026.3657142

Sajad Shirali-Shahreza;Gerald Penn

引用次数: 0