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

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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State-of-the-Art Review: Electronic Warfare Against Radar Systems

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IEEE ACCESS 2025年 13卷 57530-57567页

作者： Reddy, Reeshen Sinha, Saurabh Univ Johannesburg Fac Engn & Built Environm ZA-2094 Johannesburg South Africa Council Sci & Ind Res CSIR Dept Radar & Elect Warfare ZA-0184 Pretoria South Africa Univ Canterbury Fac Engn Christchurch 8041 New Zealand

The electromagnetic spectrum (EMS) is emerging as a sixth battlespace domain in military applications and is essential for wireless communications and remote sensing. Due to the demand for EMS, it is increasingly contested, congested, constrained, and shared. Evolving threats, doctrine, and tactics drive significant advancements in the battlespace. Electronic Warfare (EW) or Electromagnetic Warfare products need to keep pace by leveraging advances in technology driven by Industry 4.0, artificial intelligence (AI), direct radio frequency (RF) sampling, RF System on Chip (RFSoC), and ultrawideband antenna arrays. These trends drive a need for technology-intensive innovation in EW products underpinned by a coherent business-product-technology strategy. This article reviews advancements in Electronic Warfare against radar systems using a novel approach that integrates quantitative bibliometric and patentometric analyses with a qualitative assessment of EW architectures, Electronic Attack (EA) algorithms, and Electronic Support (ES) algorithms. This article marks the first instance of such a methodology being applied to systematically assess the landscape of research publications and technological innovations in the field of EW against radar systems. A taxonomy of EW System Types by military domain against radar and their role in the modern battlefield is presented together with their typical role and real-world examples. The quantitative and qualitative insights are synthesized to outline a notional next-generation EW architecture and a technology roadmap for its realization that researchers can use to guide the development of innovative EW technologies and methodologies and by EW practitioners to inform system design, operational deployment, and capability enhancements.

关键词： Radar Reviews Bibliometrics Technological innovation Market research signal processing algorithms Databases Artificial intelligence Metadata Soft sensors Digital radio frequency memory electromagnetic warfare electronic attack electronic counter measure electronic support electronic warfare Industry 4.0 radar radar countermeasures radar direction finding radar jamming strategy technology road mapping

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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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An NN Framework for Target Number Detection in FMCW Radar Systems via Hankelization: A Low-Dimensional Data Representation Perspective

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IEEE ACCESS 2025年 13卷 58823-58834页

作者： Kim, Jung-Hwan Kim, Gyung-Eun Lee, Woong-Hee Dongguk Univ Seoul Div Elect & Elect Engn Seoul 04620 South Korea

In this paper, we introduce a novel neural network (NN)-based algorithm that significantly improves the target number detection in frequency modulated continuous wave (FMCW) radar systems. By integrating the mathematical processes of Hankelization and singular value extraction, we can perform input data manipulation for effective target number detection, resulting in constructing an efficient NN framework. This is based on the following mathematical properties: 1) A sequence obtained by uniform sampling of the superposition of K radio waves can be represented as a superposition of K geometric sequences;2) A Hankelized matrix formed by the superposition of K geometric sequences exhibits low-rank characteristics;and 3) In an FMCW radar system with K targets, if the received signal, which is represented as a matrix, is ideal, the vectors obtained by extracting this matrix in row, column, diagonal, and anti-diagonal patterns can all be modeled as a superposition of K geometric sequences. The proposed NN framework showcases remarkable improvements in accuracy and efficiency for target number detection, leveraging a small sized dataset and a compact NN design to achieve unprecedented performance levels. Numerical results validate the superiority of our method across various scenarios, establishing a new benchmark for low-dimensional data representation in radar systems.

关键词： Radar Estimation Accuracy Heuristic algorithms Radar detection Radar antennas Artificial neural networks Vectors signal processing algorithms Data visualization Hankelization singular value extraction neural networks radar signal processing

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A Low-Latency and High-Performance Microwave Photonic AOA and IFM System Based on Deep Learning and FPGA

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IEEE SENSORS JOURNAL 2025年第6期25卷 9934-9945页

作者： Zhang, Longlong Li, Yin Liao, Xuan Hu, Xiang Peng, Yuanxi Zhou, Tong Natl Univ Def Technol Coll Comp Sci & Technol Changsha 410073 Peoples R China Acad Mil Sci PLA China Natl Innovat Inst Def Technol Beijing 100071 Peoples R China Natl Univ Def Technol Beijing Inst Adv Study Changsha 410073 Peoples R China

The angle of arrival (AOA) estimation and instantaneous frequency measurement (IFM) are the important aspects of radar, communication, and electronic warfare, and there is an urgent need to find intelligent solutions that are faster and more accurate than the traditional methods. Currently, deep learning (DL) has increasingly strong learning and feature extraction capabilities, which brings new opportunities to the field of electromagnetic signal processing. However, the models in DL have a large number of parameters, high storage requirements, and long computational latency and not practical for deploying applications on resource-limited devices. In this article, we build a complete AOA and IFM system for the first time, taking advantage of microwave photonics (MWPs) technology, DL, and features of field-programmable gate array (FPGA). Then, a novel hardware-friendly pruning-quantization-iteration compression (PQIC) algorithm is proposed for the post-processing of MWP signal measurement applications. By reducing the data width from 32 bits to four bits, the compressed algorithm reduces parameter storage requirements and hardware implementation complexity with negligible performance loss. Finally, we deploy the acceleration system and test the actual collected signal data on FPGA while running 200 MHz. The results show that with a 14x model compression and a 3.99x reduction in operations, the accuracy of AOA reaches 98.82%, with the mean absolute error (MAE) of 0.27 degrees. More importantly, the running latency is only 20.78 mu s, meeting the real-time processing requirements of MWP signal processing. It is also applicable to real-world signal intelligence processing and demonstrates superior performance compared to other existing algorithms in this field.

关键词： Microwave measurement Microwave theory and techniques Field programmable gate arrays Phase measurement signal processing algorithms Frequency measurement Accuracy signal processing Optical sensors Acceleration system angle of arrival (AOA) estimation deep learning (DL) field-programmable gate arrays (FPGAs) instantaneous frequency measurement (IFM) microwave photonic (MWP) microwave photonic (MWP)

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A Unified Model for Large-Scale Inexact Fixed-Point Iteration: A Stochastic Optimization Perspective

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第4期70卷 2435-2449页

作者： Hashemi, Abolfazl Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

Calculating fixed points of a nonlinear function is a central problem in many areas of science and engineering with applications ranging from the study of dynamical systems to optimization and game theory. Fixed-point iteration methods provide a simple way to calculate the fixed point of nonexpansive mappings and they have been studied extensively. Emerging applications, however, necessitate the study of fixed-point calculation under various perturbations. For instance, in the data-driven identification of dynamical systems, the learning is typically erroneous, which in turn impacts the subsequent fixed calculations, which itself is an essential step for control. Motivated by such settings, in this work, we establish a general mathematical modeling framework for the study of inexact fixed-point iteration (FPI) algorithms. In doing so, we leverage and extend the recent advances in the stochastic optimization literature to derive new methods and convergence analysis results. In particular, adopting this view enables us to present a unified mathematical model to study the impact of inexact computations in both expansive and nonexpansive scenarios, a new technical approach for the analysis of inexact FPI methods, and a new inexact FPI method, which under certain assumptions, enjoys a faster convergence rate than traditional FPI algorithms in the expansive case.

关键词： Stochastic processes Optimization Convergence Mathematical models Control systems Complexity theory Vectors signal processing algorithms Reinforcement learning Noise Gradient methods iterative algorithms iterative methods optimization methods optimization models

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LSHIM: Low-Power and Small-Area Inexact Multiplier for High-Speed Error-Resilient Applications

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IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS 2025年第1期15卷 94-104页

作者： Izadi, Azin Jamshidi, Vahid Shahid Bahonar Univ Kerman Dept Comp Engn Kerman *** Iran

Numerical computations in various applications can often tolerate a small degree of error. In fields such as data mining, encoding algorithms, image processing, machine learning, and signal processing where error resilience is crucial approximate computing can effectively replace precise computing to minimize circuit delay and power consumption. In these contexts, a certain level of error is permissible. Multiplication, a fundamental arithmetic operation in computer systems, often leads to increased circuit delay, power usage, and area occupation when performed accurately by multipliers, which are key components in these applications. Thus, developing an optimal multiplier represents a significant advantage for inexact computing systems. In this paper, we introduce a novel approximate multiplier based on the Mitchell algorithm. The proposed design has been implemented using the Cadence software environment with the TSMC 45nm standard-cell library and a supply voltage of 1.1V. Simulation results demonstrate an average reduction of 31.7% in area, 46.8% in power consumption, and 36.1% in circuit delay compared to previous works. The mean relative error distance (MRED) for the proposed method is recorded at 2.6%.

关键词： Approximation algorithms signal processing algorithms Circuits Accuracy Delays Performance evaluation Power demand Arithmetic Adders Energy consumption Approximate multiplier approximate computing Mitchell's multiplication algorithm energy-consumption area delay

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Optimal Beamforming for MIMO DFRC Systems With Transmit Covariance Constraints

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 601-616页

作者： Yang, Chenhao Wang, Xin Ni, Wei Jiang, Yi Fudan Univ Dept Commun Sci & Engn Key Lab Informat Sci Electromagnet Waves MoE Shanghai 200433 Peoples R China Commonwealth Sci & Ind Res Org CSIRO Data61 Sydney NSW 2122 Australia

This paper optimizes the beamforming design of a downlink multiple-input multiple-output (MIMO) dual-function radar-communication (DFRC) system to maximize the weighted communication sum-rate under a prescribed transmit covariance constraint for radar performance guarantee. In the single-user case, we show that the transmit covariance constraint implies the existence of inherent orthogonality among the transmit beamforming vectors in use. Then, leveraging Cauchy's interlace theorem, we derive the globally optimal transmit and receive beamforming solution in closed form. In the multi-user case, we exploit the connection between the weighted sum-rate and weighted minimum mean squared error (MMSE) to reformulate the intended problem, and develop a block-coordinate-descent (BCD) algorithm to iteratively compute the transmit beamforming and receive beamforming solutions. Under this approach, we reveal that the optimal receive beamforming is given by the classic MMSE one and the optimal transmit beamforming design amounts to solving an orthogonal Procrustes problem, thereby allowing for closed-form solutions to subproblems in each BCD step and fast convergence of the proposed algorithm to a high-quality (near-optimal) overall beamforming design. Numerical results demonstrate the superiority of our approach to the existing methods, with at least 40% higher sum-rate under a multi-user MIMO setting in the high signal-to-noise regime.

关键词： Array signal processing Radar Covariance matrices Vectors Radar antennas Downlink Transmitting antennas signal to noise ratio signal processing algorithms System analysis and design Dual-function radar-communication Cauchy's interlace theorem block coordinate descent orthogonal Procrustes problem

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Transient Electromagnetic Data Noise Suppression Method Based on MPA-VMD-SVD

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IEEE ACCESS 2025年 13卷 18890-18898页

作者： Li, Yuheng Zhang, Yang Shen, Jiwei Wen, Xinze Chen, Jianmei Zhu, Wanqiang Northeast Normal Univ Sch Phys Changchun 130000 Peoples R China Jilin Univ Sch Instrument Sci & Elect Engn Changchun 130000 Peoples R China Changchun Vocat & Tech Coll Changchun 130000 Peoples R China

The transient electromagnetic method (TEM) is an efficient physical detection method widely used in underground space detection. However, electromagnetic noise interference poses significant challenges, as the TEM late signal is often submerged in noise, severely impacting the detection accuracy and depth. Therefore, this study proposes a TEM data noise suppression method based on the marine predators algorithm (MPA) to optimize variational mode decomposition (VMD) combined with singular value decomposition (SVD). Firstly, MPA is employed to select the main parameters of VMD. Secondly, the noisy data are decomposed into several intrinsic mode functions using the adaptive variational property of VMD. Finally, the mode containing signal information undergoes SVD to remove residual noise, after which the denoised TEM signal is reconstructed. This study simulates TEM signals with different noise levels for testing. The proposed method is compared to stacking-averaging, wavelet threshold denoising, SVD, empirical mode decomposition, and unoptimized VMD. The results showed that the model exhibits superior noise reduction performance. In addition, measured noise experiments are conducted to verify the practicability of the method. Simulation and field experiments indicated that MPA-VMD-WTD is an effective method for suppressing TEM data noise.

关键词： Noise reduction signal processing algorithms Optimization Electromagnetics Lagrangian functions Transient analysis signal to noise ratio Noise measurement Feature extraction Convolutional neural networks Electromagnetic data noise reduction VMD MPA SVD

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DOA Estimation for Underwater Acoustic Array in Impulsive Noise Based on Adaptive Kernel Width Mixture Correntropy

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Dai, Zehua Wu, Jinqiu Yin, Jingwei Zhang, Wenbo Qi, Xiaofei Qiao, Gang Peng Cheng Lab Dept Strateg & Adv Interdisciplinary Res Shenzhen 518055 Peoples R China Harbin Engn Univ Coll Underwater Acoust Engn Harbin 150001 Peoples R China

This article investigates a direction-of-arrival (DOA) estimation method for underwater acoustic arrays in non-Gaussian impulsive noise environments. Traditional DOA estimation methods for underwater acoustic arrays typically presume that underwater environmental noise follows a Gaussian distribution. This assumption can lead to a significant degradation in estimation accuracy, or even failure, in underwater environments where non-Gaussian impulsive noise is predominant, thereby limiting the detection capabilities of underwater acoustic arrays. To address this issue, this study employs a method based on mixture correntropy, which maximizes the mixture correntropy of the residual fitting error matrix for subspace decomposition of the received data matrix, effectively filtering out impulsive noise. Considering the signal processing performance of correntropy and mixture correntropy depends on the selection of the kernel width, this article introduces a novel adaptive method for updating the kernel width. This method updates the kernel width in each iteration based on the residual fitting error value, setting the square of the kernel width to the sum of the squares of a preset kernel width and the residual fitting error modulus. This approach retains the simplicity and robustness of the maximum mixture correntropy criterion (MMCC) algorithm while enhancing the convergence rate and achieving a lower steady-state excess mean square error. Furthermore, this study applies the classical multiple signal classification (MUSIC) algorithm for DOA estimation. Finally, simulations and sea trials have substantiated the correctness and effectiveness of the method proposed in this article.

关键词： Noise Estimation Direction-of-arrival estimation Kernel Underwater acoustics Filtering signal processing algorithms Multiple signal classification Acoustic arrays Array signal processing Adaptive kernel width direction-of-arrival (DOA) estimation mixture correntropy non-Gaussian impulsive noise underwater acoustic arrays

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