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Underwater Acoustic signal Denoising algorithms: A Survey of the State of the Art

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Gao, Ruobin Liang, Maohan Dong, Heng Luo, Xuewen Suganthan, Ponnuthurai N. Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Natl Univ Singapore Dept Civil & Environm Engn Cluny Rd Singapore 119077 Singapore Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China Qatar Univ Coll Engn Kindi Ctr Comp Res Doha Qatar

Underwater acoustic signal (UAS) denoising is crucial for enhancing the reliability of underwater communication and monitoring systems by mitigating the effects of noise and improving signal clarity. The complex and dynamic nature of underwater environments presents unique challenges that make effective denoising essential for accurate data interpretation and system performance. This article comprehensively reviews recent advances in UAS denoising, focusing on its critical role in improving these systems. The review begins by addressing the fundamental challenges in UAS processing, such as signal attenuation, noise variability, and environmental impacts. It then categorizes and analyzes various denoising algorithms, including conventional, decomposition-based, and learning-based approaches, discussing their applications, strengths, and limitations. Additionally, the article reviews evaluation metrics and experimental datasets used in the field. The conclusion highlights key open questions and suggests future research directions, emphasizing the development of more adaptive and robust denoising techniques for dynamic underwater environments.

关键词： Noise reduction Noise Acoustics signal processing algorithms Reviews Underwater acoustics Discrete wavelet transforms signal processing Artificial intelligence signal denoising Deep learning (DL) denoising marine engineering signal decomposition underwater acoustic signal (UAS)

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Reinforcement Learning Based Online Algorithm for Near-Field Time-Varying IRS Phase Shift Optimization: System Evolution Perspective

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 1231-1245页

作者： Li, Zongtai Wang, Rui Liu, Erwu Tongji Univ Coll Elect & Informat Engn Shanghai 201800 Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 201804 Peoples R China

This paper proposes a reinforcement learning (RL) based intelligent reflecting surface (IRS) incremental control algorithm for a mmWave time-varying multi-user multiple-input single-output (MU-MISO) system. The research focuses on addressing the key challenge of near-field IRS design, which involves time-varying channels due to users' mobility. In practice, the optimization becomes more challenging when the components of the concatenated channel are unknown. From a higher perspective, we leverage electromagnetic information theory and manifold theory to provide a unified description of the IRS-assisted MU-MISO system. We regard the communication system as a nonlinear dynamic system on reproducing kernel Hilbert space (RKHS), upon which the approximate evolution operator is defined as observables for system evolution. The IRS phase shift optimization problem is modeled as a nonlinear system eigenvalue maximization problem. Utilizing the geometric properties of the unitary evolution operator, we define a metric space where the geodesic-based distance function satisfies the Lipschitz condition, enabling efficient exploitation of channel similarities. We transform the complex non-convex optimization problem into a low-dimensional linear contextual bandit problem. The performance of the proposed GLinUCB algorithm is evaluated through numerical simulations in various scenarios, showing its effectiveness in achieving high sum rates with fast convergence speed.

关键词： signal processing algorithms Time-varying systems Nonlinear dynamical systems Heuristic algorithms MIMO Extraterrestrial measurements Optimization Numerical models MISO Aerospace electronics Reinforcement learning evolution operator metric space geodesics intelligent reflecting surface

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Optimization of Fault Identification and Location Using Adaptive Neuro-Fuzzy Inference System and Support Vector Machine for an AC Microgrid

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IEEE ACCESS 2025年 13卷 20599-20619页

作者： Kurmaiah, A. Vaithilingam, C. Vellore Inst Technol Sch Elect Engn Chennai campus Chennai 600127 India

Conventional methods for high-impedance faults, low fault current levels, and communication delays could not properly identify the fault identification and location of an AC Microgrid. Fault identification and locating are crucial when integrating renewable energy sources with AC Microgrids. In an AC Microgrid, high-impedance faults, low-fault current levels, and communication delays are incapable approaches for fault identification and fault location. Machine learning enables rapid fault identification and location. This paper develops an adaptive neuro-fuzzy inference system and a support vector machine approach. To address these issues, lower fault current levels, detect high-impedance faults and affect communication delays. The proposed method is tested and evaluated in IEEE12BUS system, both in islanded and grid-connected modes, with execution times of 0.00202s in islanded mode and 0.0022s in grid-connected mode. The proposed adaptive neuro-fuzzy inference system method recognizes the optimal fault type. At the same time, Support vector machine identifies fault location accurately, resulting in the shortest execution time and minimal error percentage. This approach is demonstrated by using the IEEE12BUS AC Microgrid system. Hence, this approach is well-suited for real-time applications in AC Microgrid systems.

关键词： Microgrids Support vector machines Renewable energy sources Protection signal processing algorithms Machine learning Voltage measurement Transforms Power system stability Fault location Alternate current (AC) microgrid (MG) renewable energy source (RES) support vector machine (SVM) adaptive neuro-fuzzy inference system (ANFIS) machine learning (ML) fault identification (FI) faulty location (FL)

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Differentially Private Distributed Optimization Over Time-Varying Unbalanced Networks With Linear Convergence Rates

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 1138-1152页

作者： Yang, Zhen He, Wangli Yang, Shaofu East China Univ Sci & Technol State Key Lab Ind Control Technol Shanghai 200237 Peoples R China East China Univ Sci & Technol Minist Educ Key Lab Smart Mfg Energy Chem Proc Shanghai 200237 Peoples R China Huzhou Inst Ind Control Technol Huzhou 313099 Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing 210096 Peoples R China

This paper addresses the distributed optimization problem with privacy concerns over time-varying unbalanced networks, where agents collaborate to optimize the average of local objective functions while preserving the privacy of sensitive information encoded in local functions. To tackle the problem, the paper proposes a differentially private algorithm by exploiting decaying Laplace noise without requiring bounded gradients. The proposed algorithm is demonstrated to achieve linear convergence to the sub-optimal solution determined by the noise injected to gradient estimations in mean square and ensure $\epsilon$-differential privacy (DP) of local functions under carefully designed noise parameters. The inherent privacy-accuracy trade-off is revealed through both theoretical insights and simulation results. Furthermore, the image classification and deblurring problems are effectively solved with sensitive data being strictly protected through the deployment of the proposed algorithm, demonstrating the convergence and privacy-preserving performance of the algorithm.

关键词： Convergence Optimization Privacy signal processing algorithms Noise Differential privacy Vectors Linear programming Classification algorithms Training Distributed optimization differential privacy time-varying unbalanced networks

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Byzantine-Robust and Communication-Efficient Personalized Federated Learning

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 26-39页

作者： Zhang, Jiaojiao He, Xuechao Huang, Yue Ling, Qing Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China KTH Royal Inst Technol Div Decis & Control Syst S-11428 Stockholm Sweden

This paper explores constrained non-convex personalized federated learning (PFL), in which a group of workers train local models and a global model, under the coordination of a server. To address the challenges of efficient information exchange and robustness against the so-called Byzantine workers, we propose a projected stochastic gradient descent algorithm for PFL that simultaneously ensures Byzantine-robustness and communication efficiency. We implement personalized learning at the workers aided by the global model, and employ a Huber function-based robust aggregation with an adaptive threshold-selecting strategy at the server to reduce the effects of Byzantine attacks. To improve communication efficiency, we incorporate random communication that allows multiple local updates per communication round. We establish the convergence of our algorithm, showing the effects of Byzantine attacks, random communication, and stochastic gradients on the learning error. Numerical experiments demonstrate the superiority of our algorithm in neural network training compared to existing ones.

关键词： Servers Stochastic processes signal processing algorithms Data models Computational modeling Vectors Federated learning Convergence Adaptation models Robustness Personalized federated learning communication efficiency Byzantine-robustness constrained non-convex optimization

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Design of Reconfigurable Radar signal Processor for Frequency-Modulated Continuous Wave Radar

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IEEE SENSORS JOURNAL 2025年第7期25卷 11601-11612页

作者： Sim, Yunseong Heo, Jinmoo Jung, Yonchul Lee, Seongjoo Jung, Yunho Korea Aerosp Univ Dept Smart Air Mobil Goyang 10540 South Korea Korea Elect Technol Inst KETI Seongnam 13509 South Korea Sejong Univ Dept Elect Engn Seoul 05006 South Korea Sejong Univ Dept Convergence Engn Intelligent Drone Seoul 05006 South Korea Korea Aerosp Univ Sch Elect & Informat Engn Dept Smart Drone Convergence Goyang 10540 South Korea

Radar sensors are utilized in a variety of applications owing to their robust performance in harsh environments with minimal environmental constraints. Target detection is a crucial aspect of utilizing radar sensors. Radar signal processing for target detection entails fast Fourier transform (FFT) and constant false alarm rate (CFAR) operations to determine the distance and relative velocity of the target. However, the computational intensity of FFT and CFAR operations poses challenges for real-time operation. Despite the need for diverse radar specifications across different applications and operating environments, traditional radar signal processors designed for specific uses limit versatility. Therefore, this article proposes a reconfigurable radar signal processor (R-RSP) comprising FFT and CFAR intellectual property (IP) modules to dynamically accommodate different radar requirements. The FFT IP can support FFT operations on variable-length inputs, ranging from 64-point to 4096-point inputs. Similarly, the CFAR IP supports various algorithms, such as cell averaging (CA) CFAR, ordered statistics (OS) CFAR, smallest of OS (SOOS) CFAR, and greatest of OS (GOOS) CFAR, providing flexibility to select the optimal algorithm for different environments. To validate the R-RSP, we conducted detection experiments involving humans, drones, and cars utilizing a variety of radar sensors. The results demonstrate a minimum acceleration of approximately 16 times and a maximum acceleration of approximately 1130 times compared to the performance of the ARM Cortex-A53 microprocessor.

关键词： Radar Radar signal processing Sensors Fast Fourier transforms signal processing algorithms Radar detection Doppler radar Microprocessors Computer architecture Time-frequency analysis Constant false alarm rate (CFAR) fast Fourier transform (FFT) frequency-modulated continuous wave (FMCW) radar hardware implementation radar signal processor

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Subspace Constrained Variational Bayesian Inference for Structured Compressive Sensing With a Dynamic Grid

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 781-794页

作者： Liu, An Zhou, Yufan Xu, Wenkang Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China

We investigate the problem of recovering a structured sparse signal from a linear observation model with an uncertain dynamic grid in the sensing matrix. The state-of-the-art expectation maximization based compressed sensing (EM-CS) methods, such as turbo compressed sensing (Turbo-CS) and turbo variational Bayesian inference (Turbo-VBI), have a relatively slow convergence speed due to the double-loop iterations between the E-step and M-step. Moreover, each inner iteration in the E-step involves a high-dimensional matrix inverse in general, which is unacceptable for problems with large signal dimensions or real-time calculation requirements. Although there are some attempts to avoid the high-dimensional matrix inverse by majorization minimization, the convergence speed and accuracy are often sacrificed. To better address this problem, we propose an alternating estimation framework based on a novel subspace constrained VBI (SC-VBI) method, in which the high-dimensional matrix inverse is replaced by a low-dimensional subspace constrained matrix inverse (with the dimension equal to the sparsity level). We further prove the convergence of the SC-VBI to a stationary solution of the Kullback-Leibler divergence minimization problem. Simulations demonstrate that the proposed SC-VBI algorithm can achieve a much better tradeoff between complexity per iteration, convergence speed, and performance compared to the state-of-the-art algorithms.

关键词： Estimation Sparse matrices Sensors Bayes methods Heuristic algorithms Convergence signal processing algorithms Minimization Matrix decomposition Compressed sensing Subspace constrained variational Bayesian inference (SC-VBI) structured sparse signal recovery dynamic grid

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Multifeature Dynamic Weighting Moments Estimation for Boundary Layer Radar Wind Profiler

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Zhao, Di Tu, Gangyi Yang, Yang Shan, Shuguang Chen, Jinli Zhu, Xicheng Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Peoples R China

Weak atmospheric signals received by radar wind profilers (RWPs) often overlap with the clutter, making it difficult to identify pure atmospheric spectra. A solution with great potential is to develop RWPs with adaptive dynamic identification capabilities. We, therefore, propose a multifeature dynamic weighting (MFDW) method and specifically define a score function that includes spectral statistical features and their weights. This score function first calculates multiple spectral features and constructs a prediction function that maps the features to prediction values. Then, the weights are determined by Gaussian curve fitting and contributed to the predicted score calculation to extract pure atmospheric spectra. After that, the multiparameter cost function (MPCF) method is first improved by Doppler window settings and then utilized to estimate accurate spectral moments and profiles. The MFDW method is validated using RWP data from different regions and frequencies, and the extracted profiles are compared with GPS radiosonde (GPS RS) data. Research results confirm that dynamic weighting is adaptable to various overlap cases. Furthermore, Doppler wind profile estimation with this proposed method has a good match with GPS RS data. Both the detection accuracy and the running time excel many currently adopted methods. In brief, the chief contributions of this article lie in our innovative MFDW method and feature- and weights-involved score function.

关键词： Clutter Fuzzy logic Doppler effect Feature extraction Estimation Atmospheric measurements Spectral analysis signal processing algorithms Phased arrays Global Positioning System Clutter elimination Gaussian curve fitting moments estimation radar wind profilers (RWPs)

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Tensorized Graph Learning for Spectral Ensemble Clustering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第3期35卷 2662-2674页

作者： Cao, Zhe Lu, Yihang Yuan, Jinghui Xin, Haonan Wang, Rong Nie, Feiping Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Shaanxi Peoples R China

Ensemble clustering based on co-association matrices integrates multiple connective matrices from base clusterings to achieve superior results. However, these methods primarily focus on inter-sample relationships, neglecting variations across different base clusterings, potentially introducing noise. Additionally, they overlook interactions between samples and base clusterings, which are crucial for extracting common information and avoiding post-processing steps that may cause information loss and instability in clustering results. To address these issues, we propose the Tensorized Graph Learning for Spectral Ensemble Clustering (TGLSEC) model. TGLSEC stacks all connective matrices into a third-order tensor, employs Fast Fourier Transform (FFT) for encoding, and elucidates inter-relations in the frequency domain. By minimizing the tensor Schatten p-norm, TGLSEC extracts common information in the low-rank space, eliminating noise and improving the quality of the common shared graph. Incorporating Laplacian rank constraints, TGLSEC learns a common shared graph with c-connected components, directly representing the clustering structure and avoiding post-processing steps, leading to more stable clustering results. To enhance computational efficiency for large-scale datasets, TGLSEC has been expanded into a bipartite-graph-based model, TGLSEC-BG, reducing complexity and computational time. Extensive experiments on real-world datasets demonstrate that TGLSEC and TGLSEC-BG exhibit superior clustering performance and robustness to noise.

关键词： Tensors Noise Fast Fourier transforms Data mining Clustering algorithms Vectors Computational modeling signal processing algorithms Laplace equations Frequency-domain analysis Ensemble clustering Schatten p-norm tensorized graph learning bipartite graph

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Convergence and Privacy of Decentralized Nonconvex Optimization With Gradient Clipping and Communication Compression

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2025年第1期19卷 273-282页

作者： Li, Boyue Chi, Yuejie Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

Achieving communication efficiency in decentralized machine learning has been attracting significant attention, with communication compression recognized as an effective technique in algorithm design. This paper takes a first step to understand the role of gradient clipping, a popular strategy in practice, in decentralized nonconvex optimization with communication compression. We propose PORTER, which considers two variants of gradient clipping added before or after taking a mini-batch of stochastic gradients, where the former variant PORTER-DP allows local differential privacy analysis with additional Gaussian perturbation, and the latter variant PORTER-GC helps to stabilize training. We develop a novel analysis framework that establishes their convergence guarantees without assuming the stringent bounded gradient assumption. To the best of our knowledge, our work provides the first convergence analysis for decentralized nonconvex optimization with gradient clipping and communication compression, highlighting the trade-offs between convergence rate, compression ratio, network connectivity, and privacy.

关键词： signal processing algorithms Convergence Optimization Stochastic processes Privacy Differential privacy Perturbation methods Linear programming Vectors Training Communication compression convergence rate gradient clipping local differential privacy

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