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An Event-Triggered Hybrid Consensus Filter for Distributed Extended Object Tracking 一种用于分布式扩展对象跟踪的事件触发混合一致性过滤器
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-04-29 DOI: 10.1109/LSP.2025.3565369
Runyan Lyu;Yunze Cai;Lixiu Yao
{"title":"An Event-Triggered Hybrid Consensus Filter for Distributed Extended Object Tracking","authors":"Runyan Lyu;Yunze Cai;Lixiu Yao","doi":"10.1109/LSP.2025.3565369","DOIUrl":"https://doi.org/10.1109/LSP.2025.3565369","url":null,"abstract":"Motivated by the unique state characteristics of the extended object and energy constraints in distributed sensor networks, this letter proposes a novel event-triggered hybrid consensus filter for distributed extended object tracking, achieving balanced estimation-communication performance. This parallel consensus mechanism processes three consensus operations on the prior information pair, novel information pair of kinematic state, and shape parameter information pair of extent state, enabling enhanced consensus and propagation of extended object characteristics across the network. To reduce data transmission while preserving estimation performance, the proposed event-triggered strategy contains three distinct transmission tests, performed in parallel on corresponding information pairs to evaluate information loss. Simulation results of demonstrate the superior performance of the proposed filter compared with conventional triggered filters.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"2070-2074"},"PeriodicalIF":3.2,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117282","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
Steady-State Performance Analysis of the Nearest Kronecker Product Decomposition Based LMS Adaptive Algorithm 基于最近邻Kronecker积分解的LMS自适应算法稳态性能分析
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-04-29 DOI: 10.1109/LSP.2025.3565395
Lei Li;Yunfei Zheng;Zhongyuan Guo;Guobing Qian;Shiyuan Wang
{"title":"Steady-State Performance Analysis of the Nearest Kronecker Product Decomposition Based LMS Adaptive Algorithm","authors":"Lei Li;Yunfei Zheng;Zhongyuan Guo;Guobing Qian;Shiyuan Wang","doi":"10.1109/LSP.2025.3565395","DOIUrl":"https://doi.org/10.1109/LSP.2025.3565395","url":null,"abstract":"Inorder to address issues, such as convergence rate, stability, and computational complexity caused by the identification of long length impulse response systems, an effective nearest Kronecker product (NKP) decomposition strategy has been introduced and extended to various adaptive filters in recent years. However, the theoretical performance of the NKP decomposition-based adaptive filtering algorithms has not been thoroughly analyzed in these studies. In this letter, we focus on analyzing the steady-state performance of the NKP-based least mean square (NKP-LMS) algorithm and presents the theoretical upper bound of the step-size. Finally, simulation results confirm the precision of the theoretical assessment of the NKP-LMS algorithm and highlight its benefits in low-rank system identification.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1995-1999"},"PeriodicalIF":3.2,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072824","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
An Energy-Concentrated Transform for Improved Time-Frequency Representation of Seismic Signals 一种改进地震信号时频表示的能量集中变换
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-04-29 DOI: 10.1109/LSP.2025.3565164
Siyuan Wang;Ying Hu;Hui Chen;Xuping Chen
{"title":"An Energy-Concentrated Transform for Improved Time-Frequency Representation of Seismic Signals","authors":"Siyuan Wang;Ying Hu;Hui Chen;Xuping Chen","doi":"10.1109/LSP.2025.3565164","DOIUrl":"https://doi.org/10.1109/LSP.2025.3565164","url":null,"abstract":"Time-frequency (TF) analysis is a useful tool for seismic signal processing, where reliably representing the signal's TF distribution is essential for geological interpretation. However, the traditional short-time Fourier transform (STFT) offers an ambiguous TF representation. While synchroextracting methods improve TF localization, they distort the time-width and bandwidth of seismic signals, which are crucial for applications. To address this issue, we propose a novel TF analysis method, the energy-concentrated transform (ECT), aimed at enhancing TF localization while preserving the essential time-width and bandwidth features of seismic signals. First, we analyze the limitations of the STFT and synchroextracting methods. Next, we introduce an energy suppression operator that concentrates STFT's diffused spectral energy, aligning it with the signal's intrinsic time-width and bandwidth. Additionally, an energy recovery operator is proposed to ensure the consistency of spectral energy with the STFT spectrum. Numerical examples demonstrate the effectiveness of the ECT in enhancing TF localization, improving noise immunity, and preserving critical TF features, making it a promising tool for TF representation in seismic signals.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"2084-2088"},"PeriodicalIF":3.2,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117350","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 Space Representation Learning for Skeleton-Based Action Recognition 基于骨架的动作识别的双空间表示学习
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-04-28 DOI: 10.1109/LSP.2025.3564883
Yuheng Yang;Haipeng Chen;Zhenguang Liu;Sihao Hu;Yingying Jiao
{"title":"Dual Space Representation Learning for Skeleton-Based Action Recognition","authors":"Yuheng Yang;Haipeng Chen;Zhenguang Liu;Sihao Hu;Yingying Jiao","doi":"10.1109/LSP.2025.3564883","DOIUrl":"https://doi.org/10.1109/LSP.2025.3564883","url":null,"abstract":"Skeleton-based action recognition is crucial for machine intelligence. Current methods generally learn from 3D articulated motion sequences in the straightforward Euclidean space. Yet, the <italic>vanilla</i> Euclidean space may not be the optimal choice for modeling the intricate correlations among human body joints. This challenge arises from the non-Euclidean nature of human anatomy, where joint correlations often vary non-linearly during movement. To address this, we propose a dual space representation learning method. Specifically, we represent the motion sequences in Hyperbolic space, leveraging its intrinsic properties to capture the non-Euclidean latent anatomy of human motions. We then incorporate the motion features from both Hyperbolic and Euclidean spaces, allowing us to precisely model the non-linear joint correlations while effectively sketching human poses. The proposed method empirically achieves state-of-the-art performance on the NTU RGB+D 60, NTURGB+D 120, and NW-UCLA datasets.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"2104-2108"},"PeriodicalIF":3.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131692","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
DOA Estimation for Coherent and Non-Coherent Mixed Signals Using Toeplitz Diagonal Diffusion 基于Toeplitz对角扩散的相干和非相干混合信号DOA估计
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-04-28 DOI: 10.1109/LSP.2025.3565163
Wenlong Wang;Lei Zhang
{"title":"DOA Estimation for Coherent and Non-Coherent Mixed Signals Using Toeplitz Diagonal Diffusion","authors":"Wenlong Wang;Lei Zhang","doi":"10.1109/LSP.2025.3565163","DOIUrl":"https://doi.org/10.1109/LSP.2025.3565163","url":null,"abstract":"This letter introduces a new direction-of-arrival (DOA) estimation approach for mixed coherent and uncorrelated signals. The technique extracts cross-diagonal elements from the covariance matrix to form Toeplitz matrices, then averages them using a counting matrix to effectively decorrelate signals. This provides more accurate covariance estimates for subsequent subspace-based DOA estimation methods. Compared to existing approaches, the proposed method offers more degrees of freedom (DOFs) and lower computational complexity, while robustly detecting the directions of mixed coherent and uncorrelated signals under low signal-to-noise ratio (SNR) and limited snapshot conditions.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1985-1989"},"PeriodicalIF":3.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072822","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 Non-Asymptotic Analysis on the Additional Bias of Capon's Method Capon方法附加偏差的非渐近分析
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-04-28 DOI: 10.1109/LSP.2025.3565131
Jian Dong;Jinzhi Xiang;Wei Cui;Yulong Liu
{"title":"A Non-Asymptotic Analysis on the Additional Bias of Capon's Method","authors":"Jian Dong;Jinzhi Xiang;Wei Cui;Yulong Liu","doi":"10.1109/LSP.2025.3565131","DOIUrl":"https://doi.org/10.1109/LSP.2025.3565131","url":null,"abstract":"The Capon method is one of the classical direction-of-arrival (DOA) estimation methods in array signal processing. The standard analysis of the additional bias of this method is asymptotic, which assumes the number of snapshots <inline-formula><tex-math>$K$</tex-math></inline-formula> goes to infinity. This paper provides a non-asymptotic analysis for the additional bias by employing some tools from high-dimensional probability and perturbation analysis of optimization problems. We establish upper bounds for the additional bias in both expectation and tail forms, which reveal that the additional bias has an error rate of <inline-formula><tex-math>$O(K^{-frac{1}{2}})$</tex-math></inline-formula> when the number of snapshots satisfies a certain condition. We demonstrate our results by some numerical experiments.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1980-1984"},"PeriodicalIF":3.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072821","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
Multi Target Localization With Block Orthogonal Least Squares for Multistatic MIMO Radars 基于块正交最小二乘的多基地MIMO雷达多目标定位
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-04-28 DOI: 10.1109/LSP.2025.3565168
Martin Willame;Gilles Monnoyer;Hasan Can Yildirim;François Horlin;Jérôme Louveaux
{"title":"Multi Target Localization With Block Orthogonal Least Squares for Multistatic MIMO Radars","authors":"Martin Willame;Gilles Monnoyer;Hasan Can Yildirim;François Horlin;Jérôme Louveaux","doi":"10.1109/LSP.2025.3565168","DOIUrl":"https://doi.org/10.1109/LSP.2025.3565168","url":null,"abstract":"Recently, there has been a growing interest in multistatic radar configurations to improve the localization of multiple targets. Theoretically, the maximum likelihood (ML) approach enables to fuse the information provided by each radar pair to localize the different targets. However, it involves a multi-dimensional search process whose complexity exponentially grows with the number of targets. Consequently, heuristic methods, notably including the block orthogonal matching pursuit (BOMP), have been used in the multistatic radar context to approach the ML estimation greedily. Interestingly, the more accurate block orthogonal least squares (BOLS) method has not been studied in this context because the performance improvement is usually low in regard to its computational complexity. In this work, we investigate the application of BOLS to an angle-based localization of multiple targets using a multistatic multiple-input and multiple-output (MIMO) radar. First, an efficient implementation of BOLS is presented reducing its computational complexity. Then, using Monte Carlo simulations, we show evidence of the significant advantage of this efficient implementation of BOLS over BOMP in this scenario featuring highly correlated signals. The impact of radar parameters on the localization root mean square error and on the computational complexity of both algorithms is studied.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1990-1994"},"PeriodicalIF":3.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072823","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
Distributed Secure State Estimation Against Stealthy Attacks 针对隐身攻击的分布式安全状态估计
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-04-28 DOI: 10.1109/LSP.2025.3564886
Yan Yu;Wen Yang;Hongbo Yuan;Longyu Li;Chao Yang
{"title":"Distributed Secure State Estimation Against Stealthy Attacks","authors":"Yan Yu;Wen Yang;Hongbo Yuan;Longyu Li;Chao Yang","doi":"10.1109/LSP.2025.3564886","DOIUrl":"https://doi.org/10.1109/LSP.2025.3564886","url":null,"abstract":"This paper investigates the issue of False Data Injection (FDI) attacks within distributed state estimation. In the network, each sensor transmits its state estimate to neighboring nodes. Based on the detection variables inherent to distributed systems, we construct a covert attack strategy to bypass data detectors and degrade the estimation performance of the system. Furthermore, we propose an enhanced stealthy attack strategy, which aims to prevent interference from the attacks of neighboring edges that otherwise counteract against each other. To improve the detection rate of attacks, a detector with a dynamic coding strategy is designed to secure data transmission. The destructiveness of the stealthy attacks and the effectiveness of the detection mechanism are demonstrated through numerical examples.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1930-1934"},"PeriodicalIF":3.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937992","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
Instantaneous Frequency Estimation via Ridge Detection in Polynomial Time and Space 基于多项式时间和空间脊检测的瞬时频率估计
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-04-28 DOI: 10.1109/LSP.2025.3564882
Igor Djurović
{"title":"Instantaneous Frequency Estimation via Ridge Detection in Polynomial Time and Space","authors":"Igor Djurović","doi":"10.1109/LSP.2025.3564882","DOIUrl":"https://doi.org/10.1109/LSP.2025.3564882","url":null,"abstract":"Application of ridge detection algorithms to various transforms and problems in time-frequency (TF) analysis has become increasingly widespread with notable application in the instantaneous frequency (IF) estimation. Metaheuristic techniques, such as simulated annealing algorithms, are commonly employed for ridge detection. In this letter, we demonstrate that ridge detection can be achieved using an instance of the Viterbi algorithm (VA). This implementation ensures a global optimum with polynomial time and space complexity. The proposed ridge detection IF estimator is compared to an alternative approach based on the VA.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1975-1979"},"PeriodicalIF":3.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072820","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
Conditional Diffusion Model for Skeleton-Based Gesture Recognition With Severe Occlusions 基于骨骼的严重遮挡手势识别条件扩散模型
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-04-23 DOI: 10.1109/LSP.2025.3563445
Jinting Liu;Minggang Gan;Yao Du;Keyi Guan;Jia Guo
{"title":"Conditional Diffusion Model for Skeleton-Based Gesture Recognition With Severe Occlusions","authors":"Jinting Liu;Minggang Gan;Yao Du;Keyi Guan;Jia Guo","doi":"10.1109/LSP.2025.3563445","DOIUrl":"https://doi.org/10.1109/LSP.2025.3563445","url":null,"abstract":"In the field of skeleton-based gesture recognition, occlusion remains a significant challenge, significantly degrading performance when key joints are occluded or disturbed. To tackle this issue, we propose DiffTrans, a practical conditional diffusion model for occlusion recognition, which enables skeleton-based gesture recognition under high occlusion by generating more likely samples. This study addresses the hand skeleton occlusion problem by framing it as a conditional denoising problem, where unoccluded data serve as observations and occluded data as repair targets. We employ a conditional diffusion model to impute the missing skeleton data and the DSTANet model, which is based on the transformer, to learn the skeleton feature representations. Research results show that the DiffTrans outperforms existing methods under various occlusion modes, maintaining high performance even in scenarios with a high missing rate.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1970-1974"},"PeriodicalIF":3.2,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072819","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
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