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

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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Energy-Efficient Power Control in D2D Networks: A Distributed ADMM Approach With Dynamic Penalty Coefficient

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第5期74卷 8238-8250页

作者： Huang, Yuting Gao, Xiaozheng Shi, Minwei Ye, Neng Yang, Kai Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing 100081 Peoples R China

Device-to-device (D2D) communication plays an important role in future networks due to the explosive growth in Internet of Things (IoT) devices. The surge of mobile devices necessitates higher data transmission rates and resource utilization efficiency. To address the requirements, developing effective power control algorithms is crucial to improve network performance and reduce energy consumption. In this work, we propose a distributed power control scheme based on the combination of the alternating direction method of multipliers (ADMM) algorithm and the successive convex approximation (SCA) algorithm. With the aim of maximizing energy efficiency, the original problem is decomposed into multiple convex subproblems through SCA, and the distributed optimization of ADMM is used to solve each subproblem, thus making the solution of the problem more efficient. In particular, a dynamic penalty coefficient strategy is also developed to improve the convergence performance of the distributed algorithm. The simulation results demonstrate that compared with centralized power control methods, the proposed method can achieve similar optimal values and effectively distribute the computational load to each device, which can support the optimization design and implementation of future D2D communication systems.

关键词： Device-to-device communication Power control Throughput Power demand Data communication signal processing algorithms Resource management Power amplifiers Internet of Things Heuristic algorithms Alternating direction method of multipliers D2D communication energy efficiency power control

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Communication-Efficient Federated Optimization Over Semi-Decentralized Networks

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IEEE TRANSACTIONS ON signal AND INFORMATION processing OVER NETWORKS 2025年 11卷 147-160页

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

In large-scale federated and decentralized learning, communication efficiency is one of the most challenging bottlenecks. While gossip communication-where agents can exchange information with their connected neighbors-is more cost-effective than communicating with the remote server, it often requires a greater number of communication rounds, especially for large and sparse networks. To tackle the trade-off, we examine the communication efficiency under a semi-decentralized communication protocol, in which agents can perform both agent-to-agent and agent-to-server communication in a probabilistic manner. We design a tailored communication-efficient algorithm over semi-decentralized networks, referred to as PISCO, which inherits the robustness to data heterogeneity thanks to gradient tracking and allows multiple local updates for saving communication. We establish the convergence rate of PISCO for nonconvex problems and show that PISCO enjoys a linear speedup in terms of the number of agents and local updates. Our numerical results highlight the superior communication efficiency of PISCO and its resilience to data heterogeneity and various network topologies.

关键词： Servers Optimization Convergence Probabilistic logic Network topology Vectors signal processing algorithms Robustness Protocols Information processing Communication efficiency semi-decentralized networks probabilistic communication models local updates

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Distributed Continuous-Time Optimization With Uncertain Time-Varying Quadratic Cost Functions

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第2期55卷 1526-1536页

作者： Jiang, Liangze Wu, Zheng-Guang Wang, Lei Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China

This article studies distributed continuous-time optimization for time-varying quadratic cost functions with uncertain parameters. We first propose a centralized adaptive optimization algorithm using partial information of the cost function. It can be seen that even if there are uncertain parameters in the cost function, exact optimization can still be achieved. To solve this problem in a distributed manner when different local cost functions have identical Hessians, we propose a novel distributed algorithm that cascades the fixed-time average estimator and the distributed optimizer. We remove the requirement for the upper bounds of certain complex functions by integrating state-based gains in the proposed design. We further extend this result to address the distributed optimization where the time-varying cost functions have nonidentical Hessians. We prove the convergence of all the proposed algorithms in the global sense. Numerical examples verify the proposed algorithms.

关键词： Cost function Heuristic algorithms Costs Time-varying systems signal processing algorithms Prediction algorithms Vectors Information exchange Eigenvalues and eigenfunctions Upper bound Adaptive gain continuous-time optimization distributed optimization quadratic function time-varying cost function uncertainties

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

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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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Synthetic Aperture Radar Imaging Meets Deep Unfolded Learning: A comprehensive review

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IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE 2025年第1期13卷 79-120页

作者： Wang, Mou Hu, Yifei Wei, Shunjun Shi, Jun Cui, Guolong Kong, Lingjiang Guo, Yongxin Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China IEEE Piscataway NJ 08854 USA City Univ Hong Kong Dept Elect Engn Hong Kong 999077 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore

Synthetic aperture radar (SAR) can obtain highresolution images without being affected by environmental visibility. The compressed sensing (CS) technique is considered to be a strong candidate for simplifying SAR system complexity and improving imaging quality. With CS, SAR imaging is addressed by optimizing an augmented object function with data fidelity and feature-oriented priors, however, suffering from the time-consuming calculation and poor adaptability. Recently, an emerging technique dubbed deep unfolded/unrolled learning, or model-driven learning, offers promise in eliminating such issues by bridging the gap between learning framework and iterative algorithms. The increasing popularity of unfolded networks in SAR inverse problems also show their potential in developing efficient and accurate imaging algorithms. This paper surveys the SAR imaging algorithms based on deep unfolding techniques. We extensively cover different imaging regimes including conventional 2-D SAR, inverse SAR (ISAR), three-dimensional SAR (3-D SAR), and automotive radar imaging. On the algorithm side, the deep unfolding frameworks are mainly categorized according to the feature-oriented regularizers, wherein, their characteristics, principle, and feasibility in SAR inverse problems are discussed in detail. By reviewing the pioneer works, we discuss and reveal the current research stage in different tasks. Finally, the limitations, challenges, and opportunities of deep unfolding techniques are discussed in different radar imaging tasks.

关键词： Imaging Radar imaging Synthetic aperture radar Radar polarimetry signal processing algorithms Image resolution Three-dimensional displays signal resolution Data models Apertures

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A New Robot Navigation Algorithm Based on Bi-Directional Collaborative Ant Colony Optimization

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IEEE SENSORS JOURNAL 2025年第8期25卷 14295-14306页

作者： Chen, Xufei Hu, Yaowei Tang, Pingping Zhang, Hui Jin, Jiong Mao, Shiwen Anhui Normal Univ Sch Phys & Elect Informat Wuhu 241002 Peoples R China Nanjing Univ Posts & Telecommun Sch Commun & Informat Engn Nanjing 210003 Peoples R China Swinburne Univ Technol Sch Engn Melbourne Vic 3122 Australia Auburn Univ Dept Elect & Comp Engn Auburn AL 36849 USA

Swarm intelligence has been widely adopted and successfully applied in the field of autonomous robot navigation. Among various swarm intelligence algorithms, ant colony optimization (ACO) has shown significant potential in addressing complex navigation challenges. However, ACO faces challenges, for example, unclear initial search direction, slow convergence, limited ant flexibility, and the need to simplify robot motion control. To address these challenges, this article presents a novel bi-directional collaborative ACO (BC-ACO) algorithm with key innovations. First, the algorithm adopts a bi-directional ant colony with forward and reverse populations, achieving effective route planning through collaborative decision-making. Second, the algorithm employs an adaptive step-size strategy and a stage-based exploration ant quantity adjustment method. These innovations optimize the balance between exploration and exploitation, accelerate convergence, and address the inefficiencies of traditional ACO methods. Additionally, this article improves the heuristic function by integrating a node distance index within the bi-directional ant colony, guiding the transition of ants between nodes and further accelerating convergence. Simulation results show that BC-ACO reduces the computation time by 73.97% and improves the convergence stability by 63.64% compared to standard ACO. Additionally, BC-ACO successfully plans optimal paths, achieving a 20.85% reduction in path length. Further integration of a local quadratic segmented B-spline (LQ-S B-spline) curve results in paths with smooth transitions, reducing robot motion complexity. In summary, BC-ACO achieves fast global convergence, high stability, and short computation time.

关键词： Heuristic algorithms Bidirectional control Convergence Splines (mathematics) Robots Optimization Navigation Indexes signal processing algorithms Collaboration Bi-directional ant colony collaborative decision-making node distance index segmented B-spline stage-based exploration ant quantity adjustment

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Semi-Tensor Product Compressed Sensing With Its Applications: A Review

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IEEE SENSORS JOURNAL 2025年第3期25卷 4096-4114页

作者： Zhou, Rongpei Li, Rongfa Wu, Yaqian Chen, Jie Hong, Jin Yu, Lisu Liu, Qiegen Zhang, Yudong Nanchang Univ Sch Informat Engn Nanchang 330031 Peoples R China Natl Univ Def Technol Coll Elect Engn Hefei 230027 Peoples R China Univ Leicester Sch Comp & Math Sci Leicester LE1 7RH England

Recently, as an emerging signal processing technology, the semi-tensor product compressed sensing (STP-CS) has attracted widespread attention in the fields of image processing, communications, and bioinformatics. This article reviews the theoretical foundations, algorithmic designs, and practical applications of STP-CS. It begins by revisiting the basic concepts of compressed sensing (CS) and the definition of the semi-tensor product (STP), followed by a detailed discussion on the theoretical model of STP-CS, optimization of the measurement matrix, and reconstruction algorithms. Furthermore, the article explores the practical applications of STP-CS in areas such as sensor nodes, visual security, image encryption, and spectrum sensing, analyzing its performance advantages and potential challenges in these fields. A comprehensive analysis indicates that STP-CS offers significant benefits in saving storage space, reducing computational complexity, and enhancing data security, making it a promising technology in the field of signal processing.

关键词： Sensors Sparse matrices Image reconstruction Size measurement signal processing Compressed sensing Optimization Data processing signal processing algorithms Reviews Compressed sensing (CS) image encryption semi-tensor product (STP) signal processing

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Towards Atomic MIMO Receivers

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2025年第3期43卷 659-673页

作者： Cui, Mingyao Zeng, Qunsong Huang, Kaibin Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China

The advancement of Rydberg atoms in quantum information technology is driving a paradigm shift from classical radio-frequency (RF) receivers to Rydberg atomic receivers. Capitalizing on the extreme sensitivity of Rydberg atoms to external electromagnetic fields, Rydberg atomic receivers are capable of realizing more precise radio-wave measurements than RF receivers to support high-performance wireless communication and sensing. Although the atomic receiver is developing rapidly in quantum-physics domain, its integration with wireless communications is at a nascent stage. In particular, systematic methods to enhance communication performance through this integration are yet to be discovered. Motivated by this observation, we propose in this paper to incorporate Rydberg atomic receivers into multiple-input-multiple-output (MIMO) communication, a prominent 5G technology, as the first attempt on implementing atomic MIMO receivers. To begin with, we provide a comprehensive introduction on the principles of Rydberg atomic receivers and build on them to design the atomic MIMO receivers. Our findings reveal that signal detection of atomic MIMO receivers corresponds to a non-linear biased phase retrieval (PR) problem, as opposed to the linear Gaussian model adopted in classical MIMO systems. Then, to recover signals from this non-linear model, we modify the Gerchberg-Saxton (GS) algorithm, a typical PR solver, into a biased GS algorithm to solve the biased PR problem. Moreover, we propose a novel Expectation-Maximization GS (EM-GS) algorithm to cope with the unique Rician distribution of the biased PR model. Our EM-GS algorithm introduces a high-pass filter constructed by the ratio of Bessel functions into the iteration procedure of GS, thereby improving the detection accuracy without sacrificing the computational efficiency. Finally, the effectiveness of the devised algorithms and the feasibility of atomic MIMO receivers are demonstrated by theoretical analysis and numerica

关键词： Receivers Atomic measurements Rydberg atoms Radio frequency Sensors Electromagnetic fields Quantum sensing Energy states Symbols signal processing algorithms Rydberg atomic receivers multiple-input-multiple-output (MIMO) quantum sensing signal detection

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A Linear Mapping Dispersion Compensation Method for Debonding Detection of Honeycomb Sandwich Structure Based on Air-Coupled Ultrasound

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Zhang, Hui Zong, Zhaoyu Rui, Xiaobo Liu, Si Tianjin Univ State Key Lab Precis Measurement Technol & Instrum Tianjin 300072 Peoples R China

During the operational lifespan of the honeycomb sandwich structure (HSS), it is necessary to monitor the structural integrity to ensure its safety. Air-coupled ultrasonic guided wave technology is an efficient and convenient noncontact detection method. Due to the large acoustic impedance difference between the air and the structure and the dispersion characteristics of guided wave in the HSS, the air-coupled signals lead to significant energy loss and are easily stacked, posing challenges for the detection of debonding defects. To tackle this challenge, this study investigates the dispersion characteristics and propagation properties of guided waves in the HSS, and the effect of debonding defects on guided wave signals is researched by finite element models. The proposed linear mapping dispersion compensation algorithm refactors the linearization dispersion relationship in the frequency domain of air-coupled guided wave signals. It effectively reconstructs the stacked signal and separates the direct wave packet. Damage probability imaging is realized by using the amplitude of the direct wave to construct the damage index (DI). The imaging evaluation indexes of intersection over union (IoU) and recall rate for debonding defects of two sizes are compared, which demonstrates an improvement in defect detection accuracy. The proposed method has strong potential for real-time monitoring applications.

关键词： Dispersion Atmospheric modeling Transducers Skin Sensors Acoustics signal processing algorithms Sandwich structures Propagation Mathematical models Air-coupled ultrasound dispersion compensation guided wave honeycomb sandwich structure (HSS) lamb wave

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