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A Framework for Compressed Weighted Nonnegative Matrix Factorization

IEEE TRANSACTIONS ON signal processing 2024年 72卷 4798-4811页

作者： Yahaya, Farouk Puigt, Matthieu Delmaire, Gilles Roussel, Gilles Univ Littoral Cote dOpale LISIC UR 4491 F-62228 Calais France Expleo Grp F-33700 Meerignac France

In this paper we propose a novel framework that successfully combines random projection or compression to weighted Nonnegative Matrix Factorization (NMF). Indeed a large body of NMF research has focused on the unweighted case-i.e., a complete data matrix to factorize-with a few extensions to handle incomplete data. Also most of these works are typically not efficient enough when the size of the data is arbitrarily large. Random projections belong to the major techniques used to process big data and although have been successfully applied to NMF, there was no investigation with weighted NMF. For this reason we propose to combine random projection with weighted NMF, where the weight models the confidence in the data (or the absence of confidence in the case of missing data). We experimentally show the proposed framework to significantly speed-up state-of-the-art NMF methods under some mild conditions when applied on various data.

关键词： Cost function Convergence Symmetric matrices Standards signal processing algorithms Iterative methods Image processing Calibration Time complexity Symbols Nonnegative matrix factorization random projections matrix sketching missing entries matrix completion

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Joint Design of Doppler Resilient Unimodular Discrete Phase Sequence Waveform and Receiving Filter for Multichannel Radar

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 4207-4221页

作者： Chen, Yun Chen, Zixuan Zhang, Yunhua Yang, Jiefang Li, Dong Chinese Acad Sci Natl Space Sci Ctr CAS Key Lab Microwave Remote Sensing Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Guangdong Shenglu Telecommun Tech Co Ltd Foshan 528100 Peoples R China

Design of discrete phase sequence waveform (DPSW) with desirable co- and cross-ambiguity function (AF) properties has been a longstanding and critical challenge in the field of high-performance multichannel electronic systems, e.g. radar systems. This paper focuses on the joint design of Doppler-resilient DPSW and receiving filter with low weighted integrated sidelobe level (WISL) for multichannel radar system. This design aims to construct DPSWs of "thumbtack" shape and all-zero AFs within the desired Range-Doppler region for both co-channels and cross-channels, respectively. A peak constraint function, i.e. the penalty function, is incorporated into the objective function to control the signal-to-noise ratio loss (SNRL) due to mismatched filtering. In the design, unimodular and discrete phase constraints are imposed on each element of the sequences, while the receiving filters are subject to the energy constraint and the mismatch constraint of SNRL. Different constraints on transmitted sequences and receiving filters make the optimization problem difficult to solve. Here, an alternatively iterative algorithm based on the majorization-minimization (MM) and the coordinate descent (CD) frameworks is proposed to handle the differently constrained optimization problem. Moreover, by incorporating a general acceleration scheme and the fast Fourier transform (FFT), the computational efficiency of the proposed algorithm can be further improved. Simulation and practical experiments are conducted to validate the designed DPSWs showing superior performance when compared to that by the latest and representative methods.

关键词： Radar Filtering algorithms signal processing algorithms Radar imaging Filter banks Filtering theory Radar polarimetry Multichannel radar discrete phase sequences receiving filters Doppler tolerance majorization-minimization coordinate descent alternatively iterative

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A Communication-Efficient Adaptive Algorithm for Federated Learning Under Cumulative Regret

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 735-743页

作者： Salgia, Sudeep Gabay, Tamir Zhao, Qing Cohen, Kobi Cornell Univ Sch Elect & Comp Engn Ithaca NY 14850 USA Ben Gurion Univ Negev Sch Elect & Comp Engn Beer Sheva 8410501 Israel

We consider the problem of online stochastic optimization in a distributed setting with $M$ clients connected through a central server. We develop a distributed online learning algorithm that achieves order-optimal cumulative regret with low communication cost measured in the total number of bits transmitted over the entire learning horizon. This is in contrast to existing studies which focus on the offline measure of simple regret for learning efficiency. The holistic measure for communication cost also departs from the prevailing approach that separately tackles the communication frequency and the number of bits in each communication round.

关键词： Costs signal processing algorithms Servers Federated learning Optimization Noise measurement Estimation communication efficiency cumulative regret

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Sampling of the Wiener Process for Remote Estimation Over a Channel With Unknown Delay Statistics

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IEEE-ACM TRANSACTIONS ON NETWORKING 2024年第3期32卷 1920-1935页

作者： Tang, Haoyue Sun, Yin Tassiulas, Leandros Yale Univ Dept Elect Engn New Haven CT 06511 USA Auburn Univ Dept Elect & Comp Engn Auburn AL 36849 USA

In this paper, we study an online sampling problem of the Wiener process. The goal is to minimize the mean squared error (MSE) of the remote estimator under a sampling frequency constraint when the transmission delay distribution is unknown. The sampling problem is reformulated into an optional stopping problem, and we propose an online sampling algorithm that can adaptively learn the optimal stopping threshold through stochastic approximation. We prove that the cumulative MSE regret grows with rate O(ln k), where k is the number of samples. Through Le Cam's two point method, we show that the worst-case cumulative MSE regret of any online sampling algorithm is lower bounded by Omega(ln k). Hence, the proposed online sampling algorithm is minimax order-optimal. Finally, we validate the performance of the proposed algorithm via numerical simulations.

关键词： Delays Network interfaces Sensors signal processing algorithms Receivers Optimized production technology Optimization Age of Information online learning stochastic approximation

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Experimental Study on a Novel Machine Learning-Assisted Covariance-Based Spectrum Sensing Algorithm in the Presence of Noise

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IEEE SENSORS JOURNAL 2024年第16期24卷 26926-26934页

作者： Sabra, Ali Berbineau, Marion Inst Rech Technol Railenium F-59300 Famars France Univ Gustave Eiffel COSYS LEOST F-59650 Villeneuve Dascq France

Spectrum sensing (SS) plays a pivotal role in cognitive radio (CR) systems, facilitating efficient spectrum utilization. Covariance-based spectrum sensing (CSS) techniques have demonstrated superior performance compared with traditional methods. Recently, the integration of machine learning (ML) models has further improved CSS algorithms. In this article, we introduce a novel ML-assisted CSS algorithm and evaluate its efficacy using real-world datasets, considering practical hardware imperfections. In addition, we examine its performance in the presence of the $\alpha $ -stable distributed noise, in its impulsive and white Gaussian forms. In addition, we conduct comparative experiments with various ML-aided CSS techniques. Our experimental results consistently highlight the superiority of the proposed ML-assisted CSS algorithm over alternative methods, regardless of the ML model employed. Furthermore, our findings underscore the impact of realistic noise conditions, attributed to hardware imperfections and correlated noise, on the performance of CSS algorithms. This insight emphasizes the importance of considering practical factors and different types of noise in the design and evaluation of SS techniques.

关键词： Noise Sensors Eigenvalues and eigenfunctions Covariance matrices Machine learning algorithms signal processing algorithms Radio frequency Cognitive radio (CR) impulsive noise (IN) machine learning (ML) sensor signal processing spectrum sensing (SS)

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Maximum-Likelihood Active Device Detection and Channel Estimation in One-Bit MIMO System

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IEEE signal processing LETTERS 2025年 32卷 141-145页

作者： Ge, Mingye Liu, Yatao Shao, Mingjie Zhang, Haixia Shandong Univ Sch Informat Sci & Engn Qingdao 266237 Peoples R China Shanghai Huawei Technol Co Ltd Shanghai 200000 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Shandong Univ Shandong Key Lab Intelligent Commun & Sensing Comp Jinan 250061 Peoples R China

The future machine-type communication in internet-of-things (IoT) systems involves a massive number of devices sporadically communicating with a base station (BS) equipped with multiple antennas. Detecting active devices and estimating their associated channels are crucial but challenging due to the large number of potential devices and the small fraction of active devices. Existing studies assume high-resolution analog-to-digital converters (ADCs) at the BS, while there is a growing interest in implementing low-resolution ADCs, particularly one-bit ADCs, in massive multiple-input multiple-output (MIMO) systems. This paper focuses on the joint one-bit active device detection and channel estimation problem. We consider the maximum-likelihood approach and propose a novel expectation maximization (EM) algorithm with acceleration. On the theoretical aspect, we provide the convergent computational complexity analysis for the accelerated EM algorithm. The proposed method, evaluated through numerical simulations, outperforms benchmark algorithms in terms of both estimation accuracy and computational complexity.

关键词： Channel estimation Maximum likelihood estimation signal processing algorithms Convergence Computational complexity Maximum likelihood detection Quantization (signal) Optimization Electronic mail Antennas Active device detection channel estimation maximum likelihood expectation-maximization

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Independent Components Time-Frequency Purification With Channel Consensus Against Adversarial Attack in SSVEP-Based BCIs

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IEEE signal processing LETTERS 2025年 32卷 116-120页

作者： Yi, Hangjie Qian, Jingsheng Ming, Yuhang Kong, Wanzeng Dianzi Univ Sch Comp Sci Key Lab Brain Machine Collaborat Intelligence Zhej Hangzhou 310005 Peoples R China

The Steady State Visual Evoked Potential (SSVEP) paradigm has been widely employed in various Brain-Computer Interface (BCI) systems. However, recent studies indicate that SSVEP is vulnerable to adversarial attacks, resulting in manipulated results and drastic degradation in recognition performance, which pose inconveniences and even risks to users. Noticing the fact that the adversarial attack on SSVEP is done by adding subtle waveform perturbations into random EEG channels, we propose Independent Components Time-Frequency Purification with Channel Consensus (ICTFP-CC) as a defensive strategy. In particular, we first detect and remove suspicious perturbations with independent component analysis from the time and frequency domain, and then reconstruct the purified EEG signals. Additionally, we introduce a voting mechanism to achieve channel consensus and enhance overall robustness. We conducted experiments on two public datasets and three SSVEP recognition algorithms. The results demonstrate that our method can significantly improve the classification accuracy and information transfer rate of attacked SSVEP signals by a maximum of 46.79 (%) and 62.87 (bits/min).

关键词： Perturbation methods Electroencephalography Brain modeling Time-frequency analysis Purification signal processing algorithms Transforms Correlation Computational modeling Benchmark testing Adversarial attack electroencephalogram purification steady state visual evoked potential (SSVEP)

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Modified PUMA/EPUMA Based on Forward and Backward Linear Prediction for DOA Estimation

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Ma, Biyun Zhu, Fu Wang, Yide Zhu, Qingqing Liu, Jiaojiao South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Peoples R China Minist Nat Resources Key Lab Marine Environm Survey Technol & Applicat Guangzhou 510300 Peoples R China Nantes Univ Inst Elect & Technol NumeR IETR CNRS UMR 6164 F-44306 Nantes France

The principal-singular-vector utilization for modal analysis (PUMA) and its modification (Mod-PUMA), which utilize forward linear prediction (FLP) to process the signal subspace, experience significant performance degradation if there are multiple coherent sources and such a performance degradation will be further aggravated in low-SNR regions, which is primarily attributed to the outliers arising from inaccurate estimations of the signal subspace. To address these issues, we propose an extension version of PUMA-related algorithms, called FBLP-Mod-PUMA/enhanced-PUMA (EPUMA). The proposed algorithms improve the threshold performance by refining the signal subspace through forward and backward linear prediction (FBLP), effectively mitigating subspace leakage when dealing with coherent sources. The number of resolvable coherent sources has been theoretically derived and simulation results are provided to show the performance of the proposed algorithms.

关键词： Covariance matrices Eigenvalues and eigenfunctions Vectors Prediction algorithms Direction-of-arrival estimation Smoothing methods Simulation signal to noise ratio signal processing algorithms Sensor arrays Coherent sources direction-of-arrival (DOA) estimation forward and backward linear prediction (FBLP) principal-singular-vector utilization for modal analysis (PUMA)/enhanced-PUMA (EPUMA)

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Consensus Iterated Posterior Linearization Filter for Distributed State Estimation

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IEEE signal processing LETTERS 2025年 32卷 561-565页

作者： Garcia-Fernandez, Angel F. Battistelli, Giorgio Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ England Univ Firenze Dipartimento Ingn Informaz DINFO I-50139 Florence Italy

This paper presents the consensus iterated posterior linearisation filter (IPLF) for distributed state estimation. The consensus IPLF algorithm is based on a measurement model described by its conditional mean and covariance given the state, and performs iterated statistical linear regressions of the measurements with respect to the current approximation of the posterior to improve estimation performance. Three variants of the algorithm are presented based on the type of consensus that is used: consensus on information, consensus on measurements, and hybrid consensus on measurements and information. Simulation results show the benefits of the proposed algorithm in distributed state estimation.

关键词： Covariance matrices Approximation algorithms Vectors Kalman filters signal processing algorithms Sensors Consensus algorithm State estimation Current measurement Noise measurement Consensus distributed state estimation iterated posterior linearisation nonlinear filtering

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Multiple Dimensional Correntropy Kalman Filter

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IEEE signal processing LETTERS 2025年 32卷 1011-1015页

作者： Wang, Guoqing Zhu, Zhaolei Yang, Chunyu Ma, Lei China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China

The letter addresses the robust state estimation problem of non-Gaussian systems disturbed by outliers. Unlike the existing correntropy-based state estimation framework, which uses a uniform weight for the evaluated error vectors and solely relies on inaccurate nominal covariance matrices for estimating the system state, this work proposes a novel maximum-correntropy Kalman filter. This new approach utilizes multiple dimensional correntropy to assess the similarity between vectors across different dimensions. Additionally, it adjusts the covariance matrices simultaneously by utilizing the adopted matrix similarity measure within the modified correntropy framework. Simulations on target tracking demonstrate that our proposed algorithm exhibits excellent estimation accuracy and robustness while possessing adaptive capability even under time-varying heavy-tailed noises.

关键词： Vectors Noise Covariance matrices signal processing algorithms Kernel Pollution measurement Target tracking Noise measurement Kalman filters Accuracy Multiple dimension correntropy non-Gaussian noise robust Kalman filter covariance estimation

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