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Evolutionary Strategy-Based Location Algorithm for High-Resolution Lamb Wave Defect Detection With sparse array

IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 2021年第6期68卷 2277-2293页

作者： Chen, Honglei Liu, Zenghua Gong, Yu Wu, Bin He, Cunfu Beijing Univ Technol Fac Mat & Mfg Beijing 100124 Peoples R China Beijing Univ Technol Beijing Engn Res Ctr Precis Measurement Technol & Beijing 100124 Peoples R China

Intelligent defect location algorithms based on the times-of-flight (ToFs) of Lamb waves are attractive for nondestructive testing (NDT) and structural health monitoring (SHM) of structures with large geometric sizes. Unlike the classical imaging algorithm based on projecting the amplitude information of scattering signals into a discrete spatial grid on the structure via their propagation characteristics, intelligent defect location algorithms are more efficient in specific applications. In our previous work, an intelligent algorithm for the location of defects in plates was proposed by considering the statistical, diversity, and fuzzy characteristics of the classical defect location algorithm. This approach can realize the efficient location of different defects under a suitable parameter selection. However, interfering components remain in the results, which decreases the detection resolution. Because the measurement uncertainty is directly related to the time, an optimized intelligent location algorithm is provided for the efficient defect location with Lamb waves and a sparse transducer array in this study. The defect position is identified with high resolution by analyzing the distribution of individuals. Several specific data and a fuzzy control parameter are introduced to the proposed algorithm. The K-means algorithm was adopted to realize the adaptive updating of individuals. The influence of parameter values on the detection results was analyzed. A combined analysis of the individuals was provided to ensure the detection robustness by eliminating the influence of fuzzy control parameters on the detection. Compared with the elliptic imaging algorithm, the intelligent defect location algorithm has higher location resolution and executes approximately 65 times faster.

关键词： Imaging Trajectory Transducers Scattering Fuzzy control Clustering algorithms Approximation algorithms Evolutionary strategy (ES) intelligent detection K-means algorithm Lamb waves sparse array

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High resolution 3D imaging in MIMO radar with sparse array

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MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2018年第2期29卷 745-759页

作者： Hu, Xiaowei Tong, Ningning He, Xingyu Wang, Yuchen Air Force Engn Univ Air & Missile Def Coll Xian 710051 Shaanxi Peoples R China

High resolution three-dimensional (3D) imaging method using MIMO radar with sparse array is studied in this paper. A method based on compressive sensing (CS) is firstly given. However, the CS-based method has the off-grid problem which will reduce the estimation accuracy of scatterers' position on the target. Moreover, a high dimensional measurement matrix is required in the CS-based method, which will lead to a heavy storage and computation burden. To solve the two problems of CS, a new method based on matrix completion is proposed in this paper. After reshaping the sparse 3D echo into a low-rank structured matrix, the full 3D echo can be recovered by solving a nuclear norm minimization problem. Then the accurate position of scatterers can be estimated by applying multi-dimensional harmonic retrieval methods to the full 3D echo. Finally, the high resolution 3D image of targets is reconstructed. The effectiveness of the method is validated by the results of comparative simulations.

关键词： Multiple-input multiple-output (MIMO) radar sparse array Three-dimensional (3D) imaging Compressive sensing (CS) Matrix completion (MC)

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Enhanced BNC Approach for Noncircular Signals Direction Finding With sparse array in the Scenario of Impulsive Noise

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第5期59卷 6265-6277页

作者： Dong, Xudong Sun, Meng Zhao, Jun Zhang, Xiaofei Wang, Yide Nanjing Univ Aeronaut & Astronaut Nanjing 210000 Peoples R China Nanjing Panda Elect Co Ltd Nanjing 210000 Peoples R China Univ Nantes IETR F-44306 Nantes France Tongji Univ Shanghai 201800 Peoples R China

sparse array is popular in the field of array signal processing. However, in the direction of arrival (DOA) estimation, most research on sparse array assumes Gaussian noise, resulting in a model mismatch in the practical scenarios of impulsive noise. We investigate the estimation performance of the bounded nonlinear covariance (BNC) matrix-based methods, which can be used to replace the original data covariance matrix of the received signal and achieve good robustness to the impulsive interference. We propose an enhanced BNC (EBNC) matrix with a noncircular signal for a nested array in the scenario of impulsive noise. The proposed EBNC matrix can fight against the impulsive noise outliers and its boundedness and convergence are shown. The proposed method's performance is assessed with simulations. Simulation results indicate that the proposed method provides better performance in DOA estimation than the classical BNC, correntropy-based covariance matrix, and Gaussian covariance-based methods, especially in highly impulsive noise scenario.

关键词： Estimation Covariance matrices Direction-of-arrival estimation array signal processing Sensor arrays sparse matrices Sensors Bounded nonlinear covariance (BNC) direction of arrival (DOA) estimation impulsive noise noncircular signal sparse array

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Performance Analysis of sparse array based Massive MIMO via Joint Convex Optimization

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China Communications 2022年第3期19卷 88-100页

作者： Mengting Lou Jing Jin Hanning Wang Dan Wu Liang Xia Qixing Wang Yifei Yuan Jiangzhou Wang Future Research Lab China Mobile Research InstituteBeijing 100053China School of Engineering and Digital Arts University of KentCanterburyU.K.

Massive multiple-input multiple-output(MIMO)technology enables higher data rate transmission in the future mobile ***,exploiting a large number of antenna elements at base station(BS)makes effective implementation of massive MIMO challenging,due to the size and weight limits of the masssive MIMO that are located on each ***,in order to miniaturize the massive MIMO,it is crucial to reduce the number of antenna elements via effective methods such as sparse array *** this paper,a multiple-pattern synthesis is considered towards convex optimization(CO).The joint convex optimization(JCO)based synthesis is proposed to construct a codebook for ***,a criterion containing multiple constraints is developed,in which the sparse array is required to fullfill all ***,extensive evaluations are performed under realistic simulation *** results show that with the same number of antenna elements,sparse array using the proposed JCO-based synthesis outperforms not only the uniform array,but also the sparse array with the existing CO-based synthesis ***,with a half of the number of antenna elements that on the uniform array,the performance of the JCO-based sparse array approaches to that of the uniform array.

关键词： B5G 6G sparse array joint convex optimization massive MIMO system-level simulation

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A Sum-Difference Expansion Scheme for sparse array Construction Based on the Fourth-Order Difference Co-array

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IEEE SIGNAL PROCESSING LETTERS 2022年 29卷 2647-2651页

作者： Yang, Zixiang Shen, Qing Liu, Wei Cui, Wei Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Univ Sheffield Dept Elect & Elect Engn Sheffield S1 3JD England

A generalized sum-difference expansion scheme is proposed to construct sparse arrays based on the fourth-order difference co-array with increased degrees of freedom (DOFs). Different from existing structures, both the second-order sum and difference co-arrays are exploited in array construction under this scheme, leading to a large consecutive fourth-order difference co-array with its number of uniform DOFs (uDOFs) derived. To optimize the provided uDOFs, required design properties of the initial prototype arrays are discussed. Three examples are then provided to demonstrate its superior performance over existing structures in both resolution capacity and estimation accuracy.

关键词： Prototypes Sensor arrays Estimation White noise sparse matrices Signal resolution Signal processing algorithms Difference co-array DOA estimation fourth-order sparse array sum co-array

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A 2D adaptive beamforming method in sparse array

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2017年 77卷 100-104页

作者： Zeng, Wenhao Li, Hongtao Zhu, Xiaohua Chen, Cheng Nanjing Univ Sci & Technol Nanjing 210094 Jiangsu Peoples R China

This study presents an Accelerated Proximal Gradient singular value thresholding based Linearly Constrained Singular Canceler (APG-LCSC) algorithm to improve the performance of two-dimensional (2D) adaptive beamforming in sparse array. Firstly, a signal model is established based on Matrix Completion (MC), which can be proved to meet Null Space Property (NSP). Subsequently, left and right singular value vector of the received signal matrix is achieved via MC, and then a linearly constrained-singular canceler is obtained by employing the singular value vector. The proposed algorithm decreases the required number of antennas, lowers the computational complexity and has a robust performance. Computer simulations demonstrate that the proposed APG-LCSC algorithm can acquire high accuracy of 2D beamforming in sparse array. (C) 2017 Elsevier GmbH. All rights reserved.

关键词： sparse array Adaptive beamforming Matrix completion Antenna arrays Signal processing

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Joint DOA and Frequency Estimation With Sub-Nyquist Sampling in the sparse array System

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IEEE SIGNAL PROCESSING LETTERS 2018年第9期25卷 1285-1289页

作者： Liu, Liang Wei, Ping Univ Elect Sci & Technol China Ctr Cyber Secur Sch Informat & Commun Engn Chengdu 611731 Sichuan Peoples R China China Elect Technol Grp Corp Inst 10 Chengdu 610036 Sichuan Peoples R China

Several array systems along with algorithms based on sub-Nyquist sampling techniques have been extensively studied. This letter is committed to the joint frequency and direction-of-arrival estimation of more sources than sensors in a subband by using sparse arrays with sub-Nyquist sampling. The newly defined block vectorization eliminates the interference from sub-Nyquist sampling. Based on this, a novel augmented sample covariance matrix method is proposed, which enhances the spatial degrees of freedom and increases the number of identifiable sources. Simulations show that the joint estimation can he realized at a lower sampling rate with fewer sensors at the expense of a degradation in estimation performance. Such an algorithm provides a tradeoff between the expensive resource of sensors and channels, and the estimation performance.

关键词： Direction-of-arrival (DOA) estimation frequency estimation sparse array sub-Nyquist sampling

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An Improved PHD Filtering for DOA Tracking With sparse array via Unscented Transform Strategy

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第8期70卷 3174-3178页

作者： Dong, Xudong Sun, Meng Zhao, Jun Zhang, Xiaofei Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210000 Peoples R China Nanjing Univ Aeronaut & Astronaut Key Lab Dynam Cognit Syst Electromagnet Spectrum S Nanjing 210000 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201800 Peoples R China

Traditional direction of arrival (DOA) tracking methods are sensitive to the number of sources, and their performance degrades when the number of sources changes with time. To solve the problem of multi-sources time-varying DOA tracking, an improved probability hypothesis density (PHD) method for DOA tracking is proposed in this brief. We improve the particle diversity of the resampling method and enhance the tracking performance by preprocessing the predicted particles in the prediction step of the traditional PHD particle filtering method. Moreover, the proposed method is extended to sparse array scenario to further improve the DOA tracking accuracy. Performance of the algorithm can be demonstrated by simulations with better performance than the spatial smoothing PAST (SS-PAST) and existing PHD methods.

关键词： Probability hypothesis density (PHD) DOA tracking sparse array particle diversity unscented transform

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Novel DOAs estimation method based on Doppler aided Chinese remainder theorem with all phase DFT for multiple targets in sparse array

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IET RADAR SONAR AND NAVIGATION 2020年第10期14卷 1512-1520页

作者： Cao, Chenghu Zhao, Yongbo Pang, Xiaojiao Chen, Sheng Hu, Yili Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Xidian Univ Informat Sensing & Understanding Xian 710071 Peoples R China

In this study, the authors take further insight into the sparse geometry which offers a larger array aperture than uniform linear array with the same number of physical sensors. A novel direction of arrivals (DOAs) estimation model with flexible sparse geometry, which possesses the potential to significantly improve the estimation performance especially when the placed space and the weight of carrier such as airborne radar are restricted, is proposed to offer a larger aperture compared with co-prime array. The proposed algorithm can estimate DOAs by solving phase ambiguity. To improve the capability of spectrum analysis in frequency domain, all phase discrete Fourier transform (DFT), which can effectively alleviate spectrum leakage compared with traditional DFT, is proposed to apply into DOAs estimation. Additionally, the performance on degrees of freedom can be considerably improved compared with the state of the art where all the targets can be distinguished by Doppler information of received echo signal. More importantly, the proposed algorithm can effectively deal with DOA-closely-spaced targets because the proposed algorithm does not require to estimate signal subspace with ill-conditioned steering matrices. Both the theoretical analysis and simulation results demonstrate that the proposed algorithm significantly improves DOAs estimation precision with less computation cost.

关键词： discrete Fourier transforms airborne radar array signal processing direction-of-arrival estimation matrix algebra novel DOAs estimation method Chinese remainder theorem phase DFT multiple targets sparse array larger array aperture uniform linear array physical sensors arrivals estimation model flexible sparse geometry multiply targets estimation performance placed space airborne radar larger aperture co-prime array phase ambiguity frequency domain Doppler frequency information received echo signal spectrum analysis phase discrete Fourier spectrum leakage traditional DFT Doppler information DOA-closely-spaced targets DOAs estimation precision

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Robust Variational Bayesian Inference for Direction-of-Arrival Estimation With sparse array

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第8期71卷 8591-8602页

作者： Liu, Ying Zhang, Zongyu Zhou, Chengwei Yan, Chenggang Shi, Zhiguo Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Peoples R China Zhejiang Univ Int Joint Innovat Ctr Haining 314400 Peoples R China

Conventional direction-of-arrival (DOA) estimation algorithms are sensitive to array imperfections and outliers, making it challenging to realize accurate estimates in real applications. Facing the challenge, we propose a robust variational Bayesian inference based DOA estimation algorithm using the linear sparse array in this paper, where accurate DOA estimation with increased number of degrees of freedom (DOFs) is realized. With the mixture of von Mises model, a prior-grid scheme is further proposed to alleviate the computational burden introduced by the Bayesian variational inference framework. Since there is no restriction on the prior knowledge of the number of sources, the proposed algorithm is friendly to actual scenarios. Simulation results demonstrate the effectiveness of the proposed algorithm.

关键词： Direction-of-arrival estimation Estimation Sensor arrays Sensors Bayes methods Inference algorithms sparse matrices Direction-of-arrival estimation gain-phase error outlier sparse array variational Bayesian inference

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