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Direction Finding With Sparse Subarrays: Design, algorithms, and Analysis

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第6期60卷 8149-8165页

作者： Leite, Wesley S. de Lamare, Rodrigo C. Zakharov, Yuriy Liu, Wei Haardt, Martin Pontif Catholic Univ Rio de Janeiro PUC Rio Dept Elect Engn BR-22451900 Rio De Janeiro Brazil Univ York Sch Phys Engn & Technol York YO105DD England Hong Kong Polytech Univ Dept Elect & Elect Engn Hong Kong Peoples R China Ilmenau Univ Technol Commun Res Lab D-98693 Ilmenau Germany

This article proposes design techniques for partially calibrated sparse linear subarrays and algorithms to perform direction-of-arrival (DOA) estimation. First, we introduce array architectures that incorporate two distinct array categories, namely type-I and type-II arrays. The former breaks down a known sparse linear geometry into as many pieces as we need, and the latter employs each subarray such as it fits a preplanned sparse linear geometry. Moreover, we devise two DOA estimation algorithms that are suitable for partially calibrated array scenarios within the coarray domain. The algorithms are capable of estimating a greater number of sources than the number of available physical sensors, while maintaining the hardware and computational complexity within practical limits for real-time implementation. To this end, we exploit the intersection of projections onto affine spaces by devising the generalized coarray multiple signal classification (GCA-MUSIC) in conjunction with the estimation of a refined projection matrix related to the noise subspace, as proposed in the GCA root-MUSIC algorithm. An analysis is performed for the devised subarray configurations in terms of Degrees of Freedom, as well as the computation of the Cramer-Rao lower bound for the utilized data model, in order to demonstrate the good performance of the proposed methods. Simulations assess the performance of the proposed design methods and algorithms against existing approaches.

关键词： Estimation Direction-of-arrival estimation Geometry Sensor arrays Vectors signal processing algorithms Noise Direction-of-Arrival (DOA) estimation multiple signal classification (MUSIC) nonuniform linear arrays sparse linear array (SLA)

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Adaptive Parameter Selection Variational Mode Decomposition Based on Bayesian Optimization and Its Application to the Detection of ITSC in PMSM

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IEEE ACCESS 2024年 12卷 38594-38614页

作者： Xia, Yan-Kun Wang, Wan-Ting Li, Xin-Yang Xihua Univ Sch Elect & Elect Informat Engn Chengdu 610039 Peoples R China

Permanent magnet synchronous motor (PMSM) is a vital component of modern industry which is widely used in transport, aerospace and intelligent machinery. In the light of its relatively high frequency of inter-turn short circuit (ITSC) fault, it is valuable to detect the fault accurately and efficiently. Due to the convenience of motor vibration signal measurement, more attention has been paid to fault detection methods based on vibration signal. As a nonstationary, nonlinear signal, vibration signal is highly susceptible to external noise interference. Variational mode decomposition (VMD) has a wide range of applications in the field of nonlinear signal analysis, but the processing ability to the signal is affected by the parameter setting. Hence, a Bayesian optimized adaptive parameter selection variational mode decomposition (BOAPS-VMD) signal processing method is proposed and applied to detect the ITSC in PMSM. Firstly, the motor vibration signal is decomposed through BOAPS-VMD, and the intrinsic mode functions (IMFs) are obtained. Secondly, the cumulative variance contribution rate (C-VCR) is applied to identify the IMFs that contain fault signature information. Finally, using Hilbert transform (HT) to further analyze the IMFs that are identified. The results are output in 3D time-frequency diagrams to enhance the representation of fault signatures. The effectiveness and accuracy of the BOAPS-VMD were validated through the finite element simulation and experiment. The study shows that the BOAPS-VMD effectively improves the modal mixing phenomenon, has better noise robustness, improves the accuracy of fault detection, and has better engineering applicability.

关键词： Optimization signal processing algorithms Bayes methods Fault detection Vibrations Transforms signal resolution Parameter estimation Permanent magnet machines Hilbert space Permanent magnet synchronous motor (PMSM) fault detection variational mode decomposition (VMD) Bayesian optimization Hilbert transform (HT)

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Simpler Gradient Methods for Blind Super-Resolution With Lower Iteration Complexity

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 5123-5139页

作者： Li, Jinsheng Cui, Wei Zhang, Xu Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Xidian Univ Sch Artificial Intelligence Key Lab Collaborat Intelligence Syst Minist Educ Xian 710126 Peoples R China Xidian Univ Sch Artificial Intelligence Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710126 Peoples R China

We study the problem of blind super-resolution, which can be formulated as a low-rank matrix recovery problem via vectorized Hankel lift (VHL). The previous gradient descent method based on VHL named PGD-VHL relies on additional regularization such as the projection and balancing penalty, exhibiting a suboptimal iteration complexity. In this paper, we propose a simpler unconstrained optimization problem without the above two types of regularization and develop two new and provable gradient methods named VGD-VHL and ScalGD-VHL. A novel and sharp analysis is provided for the theoretical guarantees of our algorithms, which demonstrates that our methods offer lower iteration complexity than PGD-VHL. In addition, ScalGD-VHL has the lowest iteration complexity while being independent of the condition number. Furthermore, our novel analysis reveals that the blind super-resolution problem is less incoherence-demanding, thereby eliminating the necessity for incoherent projections to achieve linear convergence. Empirical results illustrate that our methods exhibit superior computational efficiency while achieving comparable recovery performance to prior arts.

关键词： Superresolution Complexity theory Convergence Minimization Gradient methods signal processing algorithms Sensors Optimization Vectors Radar imaging Blind super-resolution vanilla gradient descent scaled gradient descent low-rank matrix factorization

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An Invitation to Hypercomplex Phase Retrieval: Theory and applications

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IEEE signal processing MAGAZINE 2024年第3期41卷 22-32页

作者： Jacome, Roman Mishra, Kumar Vijay Sadler, Brian M. Arguello, Henry Univ Ind Santander Bucaramanga 680002 Colombia US DEVCOM Army Res Lab Adelphi MD 20783 USA Univ Ind Santander Syst Engn Dept Bucaramanga 680002 Santander Colombia

Hypercomplex signal processing (HSP) provides state-of-the-art tools to handle multidimensional signals by harnessing the intrinsic correlation of the signal dimensions through Clifford algebra. Recently, the hypercomplex representation of the phase retrieval (PR) problem, wherein a complex-valued signal is estimated through its intensity-only projections, has attracted significant interest. The hypercomplex PR (HPR) arises in many optical imaging and computational sensing applications that usually comprise quaternion- and octonion-valued signals. Analogous to the traditional PR, measurements in HPR may involve complex, hypercomplex, Fourier, and other sensing matrices. This set of problems opens opportunities for developing novel HSP tools and algorithms. This article provides a synopsis of the emerging areas and applications of HPR with a focus on optical imaging.

关键词： Correlation Algebra Image processing signal processing algorithms Optical imaging Sensors Hypercomplex Mathematics Multidimensional signal processing

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VLSI Architectures of Approximate Arithmetic Units Applied to Parallel Sensors Calibration

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年第3期71卷 1000-1013页

作者： da Rosa, Morgana Macedo Azevedo da Costa, Patricia Ucker Leleu da Costa, Eduardo Antonio Cesar Soares, Rafael I. Bampi, Sergio Univ Fed Pelotas UFPel Programade Posgrad Comp PPGC BR-96010770 Pelotas Brazil Univ Fed Rio Grande do Sul UFRGS PGMICRO BR-90010150 Porto Alegre Brazil Univ Catolica Pelotas UCPel Engn Elet & Comp MEEC BR-96015560 Pelotas Brazil

Approximate computing maximizes area and energysavings for a trade-off between quality and efficiency. Approx-imate arithmetic operators have emerged as an efficientalternative to design low-power VLSI circuits. This paper inves-tigates the design of approximate arithmetic operator unitsused in the calibration procedure for radio astronomy lightsensors - the so-called StEFCal (statistically efficient and fastcalibration) method. The StEFCal algorithm comprises arith-metic operations like a divider, square-accumulate (SAC), andmultiply-accumulate (MAC) units. The StEFCal circuit of thiswork explores the following arithmetic operators: i) two approx-imate squarer units from the literature, i.e., radix-4 (AxRSU) andSquASH, ii) two approximate iterative-based Newton-Raphson(NR) and Goldschmidt (GLD) dividers, iii) one approximateparallel prefix adder (AxPPA), and iv) a new approximateradix-4 multiplier (AxRMU), proposed in this work, exploredin the StEFCal multiply-accumulate circuit design. The AxRSUutilizes the parametersK1 andK2 to represent the number ofexact encoders for squarer- and conventional-partial products,respectively, subsequently replaced with approximate *** same principle applies to AxRMU, where the parameterKindicates the number of exact encoders for conventional-partialproducts, subsequently exchanged with approximate *** demonstrate the efficiency of StEFCal using the approximatearithmetic operators from the Pareto-optimal front that expressesthe area- and power-quality trade-off. The results show that usingthe AxRSU withK1=4 andK2=6, AxRMU, and AxPPA withK=16 and NR with one iteration has an MSE equal to 89.98dBand offers up to 158xenergy-savings compared to the exactStEFCal, and up to 25xmore energy-savings and 3.33xarea-savings compared with our previous work, 440xenergy-savingscompared to the accurate state-of-the-art, and 258xcomparedwith the approximate state-of-the-art.

关键词： Adders Calibration Approximation algorithms Arithmetic Radio astronomy Very large scale integration signal processing algorithms VLSI StEFCal AxRSU AxRMU AxPPA SquASH Newton-Raphson Goldschmidt

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Half-Duplex APs With Dynamic TDD Versus Full-Duplex APs in Cell-Free Systems

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IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第7期72卷 3856-3872页

作者： Chowdhury, Anubhab Murthy, Chandra R. Indian Inst Sci Dept Elect & Commun Engn Bengaluru 560012 India

In this paper, we present a comparative study of half-duplex (HD) access points (APs) with dynamic time-division duplex (DTDD) and full-duplex (FD) APs in cell-free (CF) systems. Although both DTDD and FD CF systems support concurrent downlink (DL) transmission and uplink (UL) reception capability, the sum spectral efficiency (SE) is limited by various cross-link interferences. We first present a novel pilot allocation scheme that minimizes the pilot length required to ensure no pilot contamination among the user equipments (UEs) served by at least one common AP. Then, we derive the sum SE in closed form, considering zero-forcing combining and precoding along with the signal-to-interference plus noise ratio optimal weighting at the central processing unit. We also present a provably convergent algorithm for joint UL-DL power allocation and UL/DL mode scheduling of the APs (for DTDD) to maximize the sum SE. Further, the proposed algorithms are precoder and combiner agnostic and come with closed-form update equations for the UL and DL power control coefficients. Our numerical results illustrate the superiority of the proposed pilot allocation and power control algorithms over several benchmark schemes and show that the sum SE with DTDD can outperform an FD CF system with similar antenna density. Thus, DTDD combined with CF is a promising alternative to FD that attains the same performance using HD APs, while obviating the burden of intra-AP interference cancellation.

关键词： Resource management Power control signal processing algorithms Scheduling Cost function Channel estimation MIMO communication Cell-free MIMO full-duplex dynamic TDD pilot allocation fractional programming

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SAOFTRL: A Novel Adaptive Algorithmic Framework for Enhancing Online Portfolio Selection

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 5291-5305页

作者： Shi, Runhao Palomar, Daniel P. Hong Kong Univ Sci & Technol HKUST Clear Water Bay Hong Kong Peoples R China

Strongly Adaptive meta-algorithms (SA-meta) are popular in online portfolio selection due to their resilience in adversarial environments and adaptability to market changes. However, their application is often limited by high variance in errors, stemming from calculations over small intervals with limited observations. To address this limitation, we introduce the Strongly Adaptive Optimistic Follow-the-Regularized-Leader (SAOFTRL), an advanced framework that integrates the Optimistic Follow-the-Regularized-Leader (OFTRL) strategy into SA-meta algorithms to stabilize performance. SAOFTRL is distinguished by its novel regret bound, which provides a theoretical guarantee of worst-case performance in challenging scenarios. Additionally, we reimagine SAOFTRL within a mean-variance portfolio (MVP) framework, enhanced with shrinkage estimators and adaptive rolling windows, thereby ensuring reliable average-case performance. For practical deployment, we present an efficient SAOFTRL implementation utilizing the Successive Convex Approximation (SCA) method. Empirical evaluations demonstrate SAOFTRL's superior performance and expedited convergence when compared to existing benchmarks, confirming its effectiveness and efficiency in dynamic market conditions.

关键词： Portfolios signal processing algorithms Accuracy Robustness Prediction algorithms Standards Heuristic algorithms Benchmark testing Adaptation models Vectors Optimistic follow-the-regularized-leader strongly adaptive meta-algorithm mean-variance portfolio universal portfolio

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Quasi-Closed-Form algorithms for Spherical Angle-of-Arrival Source Localization

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 432-448页

作者： Zhang, Tianyu Teng, Pengxiao Lyu, Jun Yang, Jun Chinese Acad Sci Inst Acoust Key Lab Noise & Vibrat Res Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

The ground curvature cannot be neglected when locating the source that generates long-range propagation signals, so the traditional angle-of-arrival (AOA) source localization evolves into spherical AOA source localization. For this problem, a quasi-closed-form algorithm, the spherical weighted pseudolinear estimator (SWPLE), is proposed in this paper. The analysis of the SWPLE reveals that the algorithm has bias problems. To mitigate the bias of the SWPLE, we propose an algorithm based on biased term estimation, the bias compensated spherical weighted pseudolinear estimator (BCSWPLE). The instrumental variable (IV) method is further introduced to solve the bias problem of the SWPLE, and the spherical weighted instrumental variables estimator (SWIVE) is proposed. The theoretical analysis shows that the SWIVE is asymptotically unbiased and achieves the Cramer-Rao lower bound (CRLB) when both the small Gaussian noise assumption and the first-order approximation are satisfied and the number of measurements is sufficiently large. The proposed algorithms can obtain quasi-closed-form solutions to the source location. Therefore, they can avoid convergence problems that are common in the maximum likelihood estimator (MLE) and achieve more robust performance. Moreover, the proposed algorithms have lower computational complexities than traditional algorithms. When there are sufficient measurements, two bias mitigation algorithms can significantly suppress the bias, among which the SWIVE can achieve similar or even better bias performance compared with the MLEs. Extensive simulations prove the proposals of the study.

关键词： Location awareness Maximum likelihood estimation signal processing algorithms Azimuth Position measurement Noise measurement Jacobian matrices Angle-of-arrival (AOA) bias compensation homogeneous least square instrumental variables pseudolinear estimator spherical source localization

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Transfer Diagnosis Model of Internal Combustion Engine With Embedded Vibration signal Impact Decomposition

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IEEE ACCESS 2024年 12卷 62779-62792页

作者： Li, He Dong, Liangyu Peng, Yang Dai, Zhixiang Jiang, Yang Zhang, Jinjie Beijing Univ Chem Technol Key Lab Engine Hlth Monitoring Control & Networkin Minist Educ Beijing 100029 Peoples R China Beijing Univ Chem Technol State Key Lab High End Compressor & Syst Technol Beijing 100029 Peoples R China China Ind Control Syst Cyber Emergency Response Te Beijing 100040 Peoples R China PetroChina Southwest Oil & Gasfield Co Gathering & Transportat Engn Technol Res Inst Chengdu 610051 Peoples R China Chongqing City Construct Investment Grp Co Ltd Chongqing 400015 Peoples R China

Traditional transfer diagnosis models for internal combustion engines show a decrease in generalization ability due to the multisource features aliasing in vibration signals and the effect of variable operating conditions. To address this problem, this paper proposes a transfer diagnosis model based on the deep subdomain adaptive network framework. To address feature aliasing, based on minimizing amplitude moment and reconstruction loss, a new adaptive decomposition layer is designed and embedded into the framework to decompose complex signals into single-impact components in time domain. To alleviate the effect of operating conditions, a new constraint for minimizing signal feature variance loss is designed and introduced into the framework's loss function. This constraint calculates the variance of the sample features of the same fault label under variable operating conditions, aiming to excavate invariant features of operating conditions and complete feature mapping of domain adaptation. Validation with experimental data yields an accuracy of 94.81%.

关键词： Artificial neural networks deep learning diesel engines fault detection feature extraction internal combustion engines signal processing algorithms time-domain analysis transfer learning vibrations

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Mixed Gas Detection and Temperature Compensation Based on Photoacoustic Spectroscopy

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2024年第2期16卷 1页

作者： Sun, Chao Hu, Runze Liu, Niansong Ding, Jianjun Jianghan Univ Wuhan 430014 Peoples R China

In recent years, with the continuous progress of technology and the development of society, the demand for updating trace gas detection technology has been increasing. The ability to quickly and accurately detect the composition and concentration of gases has become a hot topic in current research. In response to address issues such as difficulties in judging data for classification and recognizing gas components with low accuracy, a KNN-SVM algorithm has been proposed. The algorithm primarily reclassifies ambiguous data that are close to the hyperplane but do not have a clear affiliation, capturing data characteristics more comprehensively. It determines the weight ratio of each algorithm through experiments to improve the accuracy of gas category discrimination. Experimental results show that, compared to the traditional SVM algorithm, the KNN-SVM algorithm performs better in gas classification prediction, with an accuracy rate of 99.167% and an AUC indicator of 99.375%, enhancing the accuracy of gas detection. In response to the impact of temperature on the system during the experimental process, a WOA-BP temperature compensation model was established to compensate for temperature in gas concentration detection. After comparing various optimized BP neural network models, the performance of the WOA-BP temperature compensation was the most outstanding, with an R2 of 97.89%, MAE of 1.4868, RMSE of 2.0416, and a convergence speed after 15 iterations, reducing detection errors and thus achieving precise detection of low-concentration mixed gases.

关键词： Classification algorithms Support vector machines Gases Prediction algorithms Vectors signal processing algorithms Predictive models Photoacoustic spectroscopy gas detection KNN-SVM temperature compensation WOA-BP

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