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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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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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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 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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Distributed Graph Learning From Smooth Data: A Bayesian Framework

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 1626-1642页

作者： Zhang, Jiayin Wu, Nan Zhang, Tingting Li, Bin Yan, Qinsiwei Ma, Xiaoli Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

The emerging field of graph learning, which aims to learn reasonable graph structures from data, plays a vital role in Graph signal processing (GSP) and finds applications in various data processing domains. However, the existing approaches have primarily focused on learning deterministic graphs, and thus are not suitable for applications involving topological stochasticity, such as epidemiological models. In this paper, we develop a hierarchical Bayesian model for graph learning problem. Specifically, the generative model of smooth signals is formulated by transforming the graph topology into self-expressiveness coefficients and incorporating individual noise for each vertex. Tailored probability distributions are imposed on each edge to characterize the valid graph topology constraints along with edge-level probabilistic information. Building upon this, we derive the Bayesian Graph Learning (BGL) approach to efficiently estimate the graph structure in a distributed manner. In particular, based on the specific probabilistic dependencies, we derive a series of message passing rules by a mixture of Generalized Approximate Message Passing (GAMP) message and Belief Propagation (BP) message to iteratively approximate the posterior probabilities. Numerical experiments with both artificial and real data demonstrate that BGL learns more accurate graph structures and enhances machine learning tasks compared to state-of-the-art methods.

关键词： Probabilistic logic Topology Bayes methods signal processing algorithms Laplace equations Data models Symmetric matrices Message passing Vectors Uncertainty Graph learning smooth graph signal graph signal processing probabilistic model variational expectation maximization factor graph generalized approximate message passing

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Learning Flock: Enhancing Sets of Particles for Multi Substate Particle Filtering With Neural Augmentation

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 99-112页

作者： Nuri, Itai Shlezinger, Nir Ben Gurion Univ Negev Sch ECE IL-8410501 Beer Sheva Israel

A leading family of algorithms for state estimation in dynamic systems with multiple sub-states is based on particle filters (PFs). PFs often struggle when operating under complex or approximated modelling (necessitating many particles) with low latency requirements (limiting the number of particles), as is typically the case in multi target tracking (MTT). In this work, we introduce a deep neural network (DNN) augmentation for PFs termed learning flock (LF). LF learns to correct a particles-weights set, which we coin flock, based on the relationships between all sub-particles in the set itself, while disregarding the set acquisition procedure. Our proposed LF, which can be readily incorporated into different PFs flow, is designed to facilitate rapid operation by maintaining accuracy with a reduced number of particles. We introduce a dedicated training algorithm, allowing both supervised and unsupervised training, and yielding a module that supports a varying number of sub-states and particles without necessitating re-training. We experimentally show the improvements in performance, robustness, and latency of LF augmentation for radar multi-target tracking, as well its ability to mitigate the effect of a mismatched observation modelling. We also compare and illustrate the advantages of LF over a state-of-the-art DNN-aided PF, and demonstrate that LF enhances both classic PFs as well as DNN-based filters.

关键词： Radar tracking Particle measurements Atmospheric measurements signal processing algorithms Filters Vectors Heuristic algorithms Dynamical systems Accuracy Training Deep learning particle filters

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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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Fractional-Order Meta-Reinforcement Learning for Space Noncooperative Object Active Visual Tracking

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 4284-4295页

作者： Yu, Zhongliang Su, Xiaojie Sun, Guanghui Chongqing Univ Sch Automat Chongqing 400044 Peoples R China Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Peoples R China

This article primarily addresses the challenge of actively tracking noncooperative objects in aerospace, where the chaser continuously tracks noncooperative targets. Traditional approaches treat object tracking and chaser control as separate entities, and make system integration difficult. To overcome these challenges, we use fractional-order meta-reinforcement learning for active visual tracking (FracMRAVT) about noncooperative aerospace objects. FracMRAVT extracts information from the chaser's view image to generate corresponding actions and interact with the noncooperative objects. FracMRAVT demonstrates the capability for a better initial active visual tracking model compared to traditional reinforcement learning algorithms. FracMRAVT offers several advantages, including the ability to quickly adapt to other objects through the pervasiveness potential of meta-learning and memory properties of fractional calculus. Our algorithm demonstrates efficient task completion through minimal adaptation, even when dealing with other objects exhibiting different characteristics. Experimental results validate the efficacy of our algorithm, showcasing superior performance in scenarios involving both a few adaptations and nonadaptation.

关键词： Target tracking Metalearning signal processing algorithms Visualization Optimization Object tracking Fractional calculus Adaptation models Training Aerospace and electronic systems Active visual tracking fractional-order calculus meta-reinforcement learning (RL) noncooperative object

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