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

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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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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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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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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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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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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A High-Resolution TOA Estimation Algorithm Based on Coherent Subspace for Dual-Band signals

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第3期24卷 2559-2573页

作者： Fan, Jiancun Gao, Jiawei Li, Renjie Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Peoples R China

The high-resolution TOA estimation of multipath channel is essential for many areas while the resolution of conventional algorithms is constrained by the system bandwidth. With the advancement of communication technology, more applications demand higher positioning accuracy, yet single-band systems are restricted by their limited bandwidth. To address this, we consider utilizing decentralized multi-band signals to obtain a larger available bandwidth for high-resolution TOA estimation. Taking dual-band signals as an example, we exploit the dual-band coherent subspace and propose a novel subspace-based high-resolution TOA estimation algorithm. The proposed algorithm does not require parameter matching with high complexity and is suitable for the existing dual-band communication systems. We also derive the Cram & eacute;r-Rao Bound (CRB) for the dual-band signal model. Simulation results show that the performance of the proposed algorithm is better than existing subspace-based algorithms available for dual-band signals and converges to the derived CRB.

关键词： Time of arrival estimation Dual band Estimation OFDM Delay effects Accuracy signal processing algorithms Diversity reception Transceivers Simulation Time-of-arrival (TOA) estimation subspace high-resolution dual-band CRB

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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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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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