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Robust Low Sidelobe Synthesis for Arbitrary Arrays Based on Gradient Ascent With Min-Oracle

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2023年第1期71卷 450-462页

作者： Shen, Zhubin Li, Jianfeng Wu, Qihui Nanjing Univ Aeronaut & Astronaut Key Lab Dynam Cognit Syst Electromagnet Spectrum S Minist Ind & Informat Technol Nanjing 211106 Peoples R China

This article studies the robust pattern synthesis of a narrow main beam and low sidelobes of arbitrary arrays, which represents a classical but challenging problem for array antennas. In addition, pattern synthesis for a given sidelobe envelope is discussed. The synthesis problem is formulated as a nonconvex-nonconcave minimax problem with nonconvex constraints, which avoids the design of the optimal desired pattern and ensures the robustness of pattern synthesis for arbitrary arrays by nonconvex constraints. An algorithm named gradient ascent with min-oracle (GAmin) is proposed to address the considered minimax problem. Furthermore, the majorization-minimization algorithmic framework is used to reduce computational complexity. It is shown that for arbitrary linear and planar arrays, the proposed GAmin-based pattern synthesis method can perform well for pattern synthesis both without and with a given sidelobe envelope. A variety of numerical examples are provided to demonstrate the superior performance of the proposed method.

关键词： signal processing algorithms Linear antenna arrays Array signal processing Upper bound Transmission line matrix methods Robustness Computational complexity Arbitrary arrays array pattern synthesis gradient ascent with min-oracle (GAmin) minimax problem

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Gradient-Push Algorithm for Distributed Optimization With Event-Triggered Communications

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IEEE ACCESS 2023年 11卷 517-534页

作者： Kim, Jimyeong Choi, Woocheol Sungkyunkwan Univ Dept Math Suwon 16419 South Korea

Decentralized optimization problems consist of multiple agents connected by a network. The agents have each local cost function, and the goal is to minimize the sum of the functions cooperatively. It requires the agents to communicate with each other, and reducing the cost for communication is desired for a communication-limited environment. Recently, the decentralized gradient descent algorithms involving event-triggered communication have been proposed when the network of the agents is an undirected graph. On the other hand, the network of agents is often directed graph for realistic scenarios whose communication resources are limited. In this work, we first propose a gradient-push algorithm involving event-triggered communication on a directed network. Each agent sends its current states to its neighbors only when the differences between the latest sent states and the current states are larger than thresholds. The convergence of the algorithm is established under suitable decays and summability conditions on a stepsize and triggering thresholds. Numerical experiments are presented to support the effectiveness and the convergence results of the algorithm. More precisely, the numerical results reveals that the proposed algorithm may reduce the communication cost significantly compared to the gradient-push algorithm not involving the event-triggered communication.

关键词： signal processing algorithms Convergence Directed graphs Cost function Distributed databases Costs Event detection Gradient methods Decentralized optimization directed graph event-triggered communication gradient push algorithm

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Unlimited Sampling of Bandpass signals: Computational Demodulation via Undersampling

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 4134-4145页

作者： Shtendel, Gal Florescu, Dorian Bhandari, Ayush Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England

Bandpass signals are an important sub-class of bandlimited signals that naturally arise in a number of application areas but their high-frequency content poses an acquisition challenge. Consequently, "Bandpass Sampling Theory" has been investigated and applied in the literature. In this article, we consider the problem of modulo sampling of bandpass signals with the main goal of sampling and recovery of high dynamic range inputs. Our work is inspired by the Unlimited Sensing Framework (USF). In the USF, the modulo operation folds high dynamic range inputs into low dynamic range, modulo samples. This fundamentally avoids signal clipping. Given that the output of the modulo nonlinearity is non-bandlimited, bandpass sampling conditions never hold true. Yet, we show that bandpass signals can be recovered from a modulo representation despite the inevitable aliasing. Our main contribution includes proof of sampling theorems for recovery of bandpass signals from an undersampled representation, reaching sub-Nyquist sampling rates. On the recovery front, by considering both time- and frequency-domain perspectives, we provide a holistic view of the modulo bandpass sampling problem. On the hardware front, we include ideal, non-ideal and generalized modulo folding architectures that arise in the hardware implementation of modulo analog-to-digital converters. Numerical simulations corroborate our theoretical results. Bridging the theory-practice gap, we validate our results using hardware experiments, thus demonstrating the practical effectiveness of our methods.

关键词： Hardware Sensors signal processing algorithms Demodulation Baseband Radar imaging Channel coding Analog-to-digital conversion (ADC) approximation bandpass sampling modulo Shannon sampling theory

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Stolt Interpolation-Free Range Migration Algorithm for Dynamic-Aperture-Based Computational MIMO Microwave Imaging

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IEEE ACCESS 2025年 13卷 83082-83094页

作者： Skouroliakou, Vasiliki Garcia-Fernandez, Maria Alvarez-Narciandi, Guillermo Cotton, Simon Yurduseven, Okan Queens Univ Belfast Ctr Wireless Innovat Sch Elect Elect Engn & Comp Sci Belfast BT3 9DT North Ireland Univ Oviedo Dept Elect Elect Commun & Syst Engn Grp Signal Theory & Commun Gijon 33203 Spain

Computational imaging (CI) techniques realized by dynamic metasurface antennas (DMAs) offer significant advantages in the hardware design reducing the cost of such systems. In this paper, we present a physical layout based solely on DMA panels that can be used to synthesize electrically large apertures suitable for microwave imaging. Although hardware is significantly simplified, the challenge of using CI DMA-based systems presents itself in the signal processing layer. This is because in microwave CI the transfer function of the antennas is used to encode information from the imaged target scene and compress it into a single (or a few) channel(s). As a consequence, real-time Fourier-based image reconstruction algorithms cannot be directly applied because of the nature of the compressed back-scattered signals. In this work, we propose an algorithm that employs a decompression step such that the signal is converted to a suitable form for spatial frequency domain processing. The proposed technique mitigates the challenges of applying the algorithm in the near-field. Crucially, it also eliminates the need for a computationally expensive Stolt interpolation step, which can be a significant bottleneck for conventional Fourier-based image reconstruction algorithms. Numerical and experimental CI results are presented to validate the ability of the proposed system and algorithm to produce reconstructed images of good quality. The benefits of the proposed algorithm in terms of execution time and storage requirements are also explored in this work.

关键词： Imaging signal processing algorithms Image reconstruction Apertures Layout Microwave imaging Hardware Three-dimensional displays Metamaterials Interpolation Computational imaging (CI) dynamic metasurface antennas (DMAs) Fourier-based signal processing microwave imaging near-field imaging range migration algorithm (RMA)

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Distributed Alignment Processes With Samples of Group Average

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2023年第2期10卷 960-971页

作者： Korman, Amos Vacus, Robin HUJI WIS Int Joint Res Unit CNRS UMI FILOFOCSUP7French Israeli Lab Fdn Comp SciT Tel Aviv Israel Res Inst Fdn Comp Sci CNRS Paris France

This article studies a stochastic alignment problem assuming that agents can sense the general tendency of the system. More specifically, we consider $n$ agents, each being associated with a real number. In each round, each agent receives a noisy measurement of the system's average value and then updates its value. This value is then perturbed by random drift. We assume that both noise and drift are Gaussian. We prove that a distributed weighted-average algorithm optimally minimizes the deviation of each agent from the average value, and for every round. Interestingly, this optimality holds even in the centralized setting, where a master agent can gather all the agents' measurements and instruct a move to each one. We find this result surprising since it can be shown that the set of measurements obtained by all agents contains strictly more information about the deviation of Agent $i$ from the average value, than the information contained in the measurements obtained by Agent $i$ alone. Although this information is relevant for Agent $i$, it is not processed by it when running a weighted-average algorithm. Finally, we also analyze the drift of the center of mass and show that no distributed algorithm can achieve drift that is as small as the one that can be achieved by the best centralized algorithm.

关键词： Noise measurement Current measurement Weight measurement Synchronization signal processing algorithms Protocols Position measurement Biological distributed algorithms clock synchronization consensus distributed signal processing flocking Kalman filter noisy communication weighted-average algorithms

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MUSIC With Capped Frobenius Norm: Efficient Robust Direction-of-Arrival Estimator

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第6期59卷 8090-8103页

作者： Li, Xiao Peng Liu, Zhaofeng Shi, Zhang-Lei So, Hing Cheung Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China Shenzhen Univ State Key Lab Radio Frequency Heterogeneous Integr Shenzhen 518060 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China China Univ Petr East China Coll Sci Qingdao 266580 Peoples R China

Direction-of-arrival (DOA) estimation is a frequent need in the field of array signal processing. While many conventional algorithms achieve excellent performance in Gaussian noise, they are vulnerable to impulsive noise. Although several approaches have been proposed for robust DOA estimation against gross errors, their disadvantages might limit the applicability in practice. For instance, the maximum likelihood (ML) estimation-based algorithms involve high computational complexity, and & ell;(p)-multiple signal classification with p is an element of (1, 2) requires tweaking p for handling different noises. In this work, we devise a capped Frobenius norm (CFN) for complex-valued data inspired by the truncated least squares loss function. Since the cap threshold is the boundary to differentiate the normal and outlier-contaminated entries, we propose a normalized median absolute deviation-based strategy for its automatic determination. In doing so, the accurate estimation is achieved in both Gaussian and impulsive noise. As the CFN is nonconvex and nonsmooth, we exploit the half-quadratic theory to simplify the resultant problem into a tractable optimization, which is then handled by alternating convex optimization with computationally efficient closed-form solution. Furthermore, its convergence behaviors are analyzed, i.e., the objective function value is convergent, and there exists a subsequence in the variable sequence converging to a critical point. Simulation results exhibit its superior performance over several state-of-the-art algorithms in terms of estimation accuracy and resolution capability. MATLAB code is available at https://***/Li-X-P/Code-Robust-DOA-Estimator.

关键词： Direction-of-arrival estimation Multiple signal classification Computational complexity signal processing algorithms Optimization Maximum likelihood estimation Gaussian noise Capped Frobenius norm (CFN) direction-of-arrival (DOA) estimation multiple signal classification (MUSIC) proximal block coordinate descent robust recovery

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Reduced-Complexity Subarray-Level Sparse Recovery STAP for Multichannel Airborne Radar WGMTI Application

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第5期59卷 6292-6313页

作者： Cui, Ning Xing, Kun Yu, Zhongjun Duan, Keqing Chinese Acad Sci Aerosp Informat Res Inst Beijing 100864 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100094 Peoples R China Sun Yat Sen Univ SYSU Sch Elect & Commun Engn Guangzhou 510275 Peoples R China

For multichannel airborne radars, wide-area ground-moving target indication (WGMTI) processing can quickly obtain the dynamic distribution of moving targets in a wide area, which holds considerable significance in many fields. Nevertheless, the WGMTI mode suffers from the interference of powerful ground clutter, which frequently submerges slow-moving targets and causes many false alarms in subsequent moving target detection. Space-time adaptive processing (STAP) can successfully suppress clutter, but its performance depends critically on the available training samples. Consequently, an effective STAP method characterized by fast processing and a small sample size for WGMTI applications in multichannel airborne radars must be developed. In this article, a subarray-level sparse recovery STAP processing framework is proposed for multichannel airborne radars. First, the characteristics of the subarray-level received clutter are discussed in detail. Second, on the basis of this analysis, we further designed a joint space-time dictionary and developed a separable tensor-based sparse Bayesian learning (STSBL) method. In this method, two-stage decomposition is proposed to ensure that large-scale data can be degraded into small-scale data in processing, which significantly improves computation efficiency. Finally, the effectiveness of the proposed STSBL-STAP method in WGMTI processing was verified using real measurement data obtained from a developed dual-channel Ku-band airborne radar.

关键词： Clutter Dictionaries Airborne radar Training signal processing algorithms Bayes methods Optimization Airborne radar sparse recovery space-time adaptive processing (SR-STAP) subarray wide-area ground-moving target indication (WGMTI)

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Incipient Electric Circuit Failure Detection and Outage Prevention Using Advanced Electrical Waveform Monitoring FIELD EXPERIENCE

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IEEE INDUSTRY APPLICATIONS MAGAZINE 2023年第3期29卷 36-45页

作者： Russell, B. Don Benner, Carl Wischkaemper, Jeffrey Muthu-Manivannan, Karthick Texas A&M Univ College Stn TX 77843 USA

SAFE, RELIABLE ELECTRIC POWER IS CRITICALLY IMPORTANT in industrial facilities, including field production facilities and plants. The electric circuit protection and monitoring systems currently in use do not detect incipient faults or predict apparatus failures. Two decades of research demonstrate that early stages of apparatus, device, and cable failures can, in some cases, be detected electrically, enabling repairs and avoidance of faults and outages. The authors have developed a technology, which has become known as distribution fault anticipation (DFA), that uses continuous monitoring to provide real-time situational awareness of circuit activity. The DFA system monitors voltage and current signals to detect circuit abnormalities and failures and utilizes intelligent algorithms to classify circuit events, including incipient failure signatures. This article provides examples of failing devices that were successfully detected in early stages. DFA technology was originally developed in cooperation with utility companies but is applicable to any power distribution circuit and has begun trials in an industrial setting.

关键词： Monitoring Circuit faults Substations Maintenance engineering Recording Personnel signal processing algorithms

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FedGiA: An Efficient Hybrid Algorithm for Federated Learning

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 1493-1508页

作者： Zhou, Shenglong Li, Geoffrey Ye Beijing Jiaotong Univ Sch Math & Stat Beijing 100044 Peoples R China Imperial Coll London Dept Elect & Elect Engn ITP Lab London SW7 2AZ England

Federated learning has shown its advances recently but is still facing many challenges, such as how algorithms save communication resources and reduce computational costs, and whether they converge. To address these critical issues, we propose a hybrid federated learning algorithm (FedGiA) that combines the gradient descent and the inexact alternating direction method of multipliers. The proposed algorithm is more communication- and computation-efficient than several state-of-the-art algorithms theoretically and numerically. Moreover, it also converges globally under mild conditions.

关键词： signal processing algorithms Convergence Convex functions Servers Radio frequency Federated learning Optimization gradient descent inexact ADMM communication-efficiency computational efficiency global convergence

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Sleep signal Analysis for Early Detection of Alzheimer's Disease and Related Dementia (ADRD)

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第5期27卷 2264-2275页

作者： Khosroazad, Somayeh Abedi, Ali Hayes, Marie J. Univ Maine Elect & Comp Engn Orono ME 04469 USA

Objective: Alzheimer's Disease and Related Dementia (ADRD) is growing at alarming rates, putting research and development of diagnostic methods at the forefront of the biomedical research community. Sleep disorder has been proposed as an early sign of Mild Cognitive Impairment (MCI) in Alzheimer's disease. Although several clinical studies have been conducted to assess sleep and association with early MCI, reliable and efficient algorithms to detect MCI in home-based sleep studies are needed in order to address both healthcare costs and patient discomfort in hospital/lab-based sleep studies. Methods: In this paper, an innovative MCI detection method is proposed using an overnight recording of movements associated with sleep combined with advanced signal processing and artificial intelligence. A new diagnostic parameter is introduced which is extracted from the correlation between high frequency, sleep-related movements and respiratory changes during sleep. The newly defined parameter, Time-Lag (TL), is proposed as a distinguishing criterion that indicates movement stimulation of brainstem respiratory regulation that may modulate hypoxemia risk during sleep and serve as an effective parameter for early detection of MCI in ADRD. By implementing Neural Networks (NN) and Kernel algorithms with choosing TL as the principle component in MCI detection, high sensitivity (86.75% for NN and 65% for Kernel method), specificity (89.25% and 100%), and accuracy (88% and 82.5%) have been achieved.

关键词： Sleep Diseases Alzheimer's disease Feature extraction signal processing algorithms Electroencephalography Sleep apnea Alzheimer's Disease kernel estimation mild cognitive impairment neural network respiratory changes sleep movements

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