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Toward sparse planar array Underwater Acoustical Imaging Using Compressive Sensing Pattern Matching Technique

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Menakath, Mimisha M. Panicker, Mahesh Raveendranatha Hareesh, G. Peter, Rubin Indian Inst Technol Palakkad Dept Elect Engn Palakkad 678623 India Indian Inst Technol Palakkad Palakkad 678623 India Singapore Inst Technol Infocomm Technol Cluster Singapore 138683 Singapore Naval Phys & Oceanog Lab Kochi 682021 India

3-D underwater acoustical imaging is an emerging technology used for ocean exploration and underwater vehicle navigation. Achieving high-resolution 3-D imaging is challenging due to the requirement of a substantial number of sensor elements, resulting in both computational and hardware complexities. A practical solution to this issue is the utilization of sparse arrays, which can exhibit performance akin to that of uniform arrays. Although designing sparse arrays for narrowband signals is relatively straightforward, designing sparse arrays for wideband signals, which is typically employed in imaging sonars, is challenging due to the frequency-dependent variation in the array response. In this work, an efficient and low complex approach is proposed for creating sparse wideband arrays with a frequency-dependent array response. The methodology employs the multitask Bayesian compressive sensing (MTBCS) algorithm to determine the minimum number of sparse sensor locations and weights with a minimum level of l(2) norm pattern matching error between the desired pattern and the pattern synthesized by the estimated weights. Compared to conventional narrowband design methods and least-squares solutions, this approach offers comparable sparsity with a minimum mean square error (mse). It can be applied to obtain frequency-dependent or frequency-invariant patterns for wideband signals. The performance of the proposed algorithm is demonstrated through various numerical simulations and experiments through the reconstruction of 2-D images of different underwater targets using uniform and sparse linear arrays. The proposed method has achieved up to 90.8% element saving for the test cases.

关键词： Wideband Sensor arrays array signal processing Imaging Compressed sensing Bayes methods Vectors Pattern matching Three-dimensional displays planar arrays Multitask Bayesian compressive sensing (MTBCS) pattern matching sparse planar array underwater 3-D imaging wideband sparse array

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Optimization of High-Resolution and Ambiguity-Free sparse planar array Geometry for Automotive MIMO Radar

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 4332-4348页

作者： Huan, Mingsai Liang, Junli Ma, Yugang Liu, Wei Wu, Yifan Zeng, Yonghong Ocean Univ China Fac Informat Sci & Engn Qingdao 266100 Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian 710129 Peoples R China ASTAR Inst Infocomm Res I2R Singapore 138632 Singapore Hong Kong Polytech Univ Dept Elect & Elect Engn Kowloon Hong Kong 999077 Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

The next-generation 4D imaging automotive radar is characterized by high angular resolution, unambiguous detection, low latency, low cost, and small size. This study provides an enhanced analysis of the angular ambiguity function (AAF) for planar MIMO arrays, and pioneers a method for a more accurate evaluation of angular resolution using the main lobe width (MLW). Then the 2D expanded beam pattern (EBP) is introduced to assess the field-of-view (FOV), region of interest (ROI), sidelobe level (SLL), and normalized resolution intuitively and precisely. After constructing the sophisticated 2D element spacing and aperture constraints for planar MIMO arrays, the optimization of array geometry is creatively formulated as a novel Domino sparse optimization problem aiming to minimize the MLW while sufficiently suppressing the SLL, which is inspired by the sequential fall of dominoes. This non-convex and non-smooth constrained problem is efficiently solved by a hybrid optimization framework, which integrates the alternating direction multiplier method (ADMM), aggregate function, modified real genetic algorithm (MGA), and non-uniform fast Fourier transform (NUFFT). Numerical simulations demonstrate that angular resolution varies with array geometry, even under the same aperture size. The proposed arrays outperform others with equal aperture size, exhibiting narrower MLW and lower Cram & eacute;r-Rao bound (CRB), thereby enhancing angular resolution with fewer antennas and without preprocessing in standard single-snapshot 2D DOA estimation methods.

关键词： Geometry Optimization Apertures Direction-of-arrival estimation Estimation MIMO radar MIMO communication Colocated MIMO radar sparse planar array angular ambiguity function angular resolution non-convex optimization

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Micro-seismic monitoring using sparse planar array and a weak signal enhancement method

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JOURNAL OF GEOPHYSICS AND ENGINEERING 2023年第5期20卷 965-977页

作者： Yang, Xiaohui Lin, Zhengliang Yang, Xinchao Chen, Zhanguo Si, Wenpeng SINOPEC Geophys Res Inst Co Ltd Nanjing 211103 Peoples R China

Traditional ground micro-seismic monitoring is performed by laying long survey lines. This is expensive and difficult to implement in complex mountainous areas and deep marine shale gas reservoirs in China. To address these challenges, this study proposes a ground micro-seismic monitoring method using a sparse planar array that offers greater flexibility in implementation. This study presents a weak signal enhancement method based on a broadband-array adaptive beamforming algorithm to improve the signal-to-noise ratio (SNR) of micro-seismic data collected by sparse planar arrays and to suppress coherent noise. The proposed method involves establishing a signal model for a broadband planar array, estimating the direction of arrival (DOA) of broadband signals using a grid search method, and setting constraint conditions and objective functions based on the DOA results. The optimal weight vector is then calculated by solving the objective function to obtain the desired signal and suppress noise. This study demonstrates that the proposed method effectively improved the SNR and suppressed coherent noise in synthetic and real data. It also highlights the effectiveness of the sparse planar array as a ground micro-seismic monitoring method and the adaptive broadband beamforming method as a practical weak signal enhancement technology.

关键词： ground micro-seismic monitoring sparse planar array adaptive 2D planar beamforming weak signal enhancement

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Coherence Factor-Based Polarimetric Diffraction Tomography for 3-D Inverse Scattering With a sparse planar array

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Wang, Miao Sun, Shilong Dai, Dahai Zhang, Yongsheng Su, Yi Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China

A sparse planar array often suffers from the strong grating-lobes that deteriorate inversion performance due to spatial undersampling. In this article, a diffraction tomography (DT) algorithm is proposed to solve 3-D electromagnetic inverse scattering problems (EISPs) with a sparse planar array and polarization diversity. The method deals with multifrequency multipolarization data as follows: first, doing a polarization fusion;next, using the exact coordinate transformation;then decoupling and modifying the aliased multifrequency spatial spectrum of the permittivity and the conductivity;finally, applying the range-normalization 2-D coherence factor (2-D CF) weighting. The weak scatterers' geometries and dielectric properties can be well reconstructed under low-contrast conditions, and the non-weak scatterers can also be detected. The proposed method outperforms the conventional DT approach regarding grating-lobes and noise suppression without compromising quantitative inversion performance, as demonstrated by the inverted results obtained from synthetic and experimental data.

关键词： Diffraction tomography (DT) inverse scattering polarization diversity quantitative inversion sparse planar array

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Design of Wide Scanning sparse planar array Using Both Matrix-Pencil and Space-Mapping Methods

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2021年第2期20卷 140-144页

作者： Gu, Pengfei He, Z. Xu, Juan Leung, Kwok Wa Chen, R. S. Nanjing Univ Sci & Technol Dept Communicat Engn Nanjing 210094 Peoples R China Qufu Normal Univ Coll Phys & Engn Qufu 273165 Shandong Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

A hybrid efficient optimizing method of wide scanning sparse planar array involving mutual coupling is investigated in this letter. The former work of matrix enhancement and matrix pencil (MEMP)-based synthesis method is extended to the design of pattern reconfigurable sparse arrays in wide-angle scanning. The genetic algorithm was utilized to further optimize the reconstructed sparse array including the actual antenna element, which differs from the only array factor synthesis of MEMP. To improve the optimization efficiency and design accuracy, the space mapping technique was exploited to accomplish the optimizing design, which also involves the interactions among elements during the optimizing process. A wide-scanning sparse array can be realized by the new relationship of parameter mapping courses between low-cost coarse model optimizations and high-accuracy fine model validations based on the characteristic mode and multilevel fast multipole algorithm. Numerical examples show the efficiency of the proposed method.

关键词： Transmission line matrix methods planar arrays sparse matrices Matrix decomposition Eigenvalues and eigenfunctions Symmetric matrices Matrix converters Genetic algorithm matrix enhancement and matrix pencil (MEMP) mutual coupling space mapping sparse planar array wide-angle scanning

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Synthetic sparse planar array design for two-dimensional DOA estimation

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DIGITAL SIGNAL PROCESSING 2022年 120卷 103268-103268页

作者： Li, Xiangnan Wang, Weijiang Wang, Xiaohua Ren, Shiwei Beijing Inst Technol Sch Informat & Elect 5 South Zhongguancun St Beijing 100081 Peoples R China

array motion can efficiently enhance the achievable number of degrees-of-freedom (DOFs) by the virtue of filling the holes in the difference co-array. In this paper, the concept of synthetic array based on array motion is extended to two dimensional cases. A new structured sparse planar array on a moving platform named as Synthetic Augmented Cross array (SACA) is proposed. The proposed array yields a co-array with much larger virtual uniform rectangular array in it. Closed-form expressions of DOFs with different moving trails are also delineated. Then by further improving the DOFs of SACA, a series of generalized configurations named as Improved Synthetic Augmented Cross array (ISACA) are proposed. With the same number of sensors, it can achieve larger DOFs than SACA. Numerical results are provided for performance comparison and validations of analysis. (c) 2021 Elsevier Inc. All rights reserved.

关键词： DOA estimation sparse planar array array design array motion Difference co-array

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Distributed Convex Optimization Compressed Sensing Method for sparse planar array Synthesis in 3-D Imaging Sonar Systems

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IEEE JOURNAL OF OCEANIC ENGINEERING 2020年第3期45卷 1022-1033页

作者： Gu, Boxuan Chen, Yaowu Liu, Xuesong Zhou, Fan Jiang, Rongxin Zhejiang Univ Inst Adv Digital Technol & Instrumentat Hangzhou 310027 Peoples R China Zhejiang Univ Zhejiang Prov Key Lab Network Multimedia Technol Hangzhou 310027 Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

Synthesis of sparse planar arrays can effectively reduce hardware costs and computational complexity in phased array 3-D imaging sonar systems. Traditional stochastic methods, such as simulated annealing, require multiple experiments and parameter adjustments to obtain optimal results. Methods based on compressed sensing (CS) can overcome this defect. However, when applied to large arrays, CS methods require vast computational complexity and may not obtain optimal sparse results because of violating the restricted isometry property. To make CS methods more practical, a distributed convex optimization CS method is proposed here for the sparse planar array synthesis in 3-D imaging sonar systems. This method is based on the CS theory, solving the minimum number of active elements under certain beam pattern constraints using the iterative reweighted l(1)-norm minimization algorithm. Then, a multistage distributed framework is proposed to decompose the array into multistage subarrays, and the array synthesis is performed sequentially for each stage subarray to reduce computational complexity and obtain higher sparsity rates. Some applications of sparse planar array synthesis are employed to evaluate the efficiency of the proposed method.

关键词： planar arrays Convex functions Apertures Computational complexity Iterative algorithms Minimization Transmission line matrix methods Compressed sensing (CS) convex optimization multistage distributed framework phased array 3-D imaging sonar system sparse planar array

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Matrix completion-based MIMO radar imaging with sparse planar array

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SIGNAL PROCESSING 2017年第0期131卷 49-57页

作者： Hu, Xiaowei Tong, Ningning Wang, Jianye Ding, Shanshan Zhao, Xiaoru Air Force Engn Univ Air & Missile Def Coll Xian 710051 Shaanxi Peoples R China

Multiple-input multiple-output (MIMO) radar with sparse planar arrays is expected to provide one snapshot imaging of complex motion targets at low hardware costs. In recent years, compressive sensing (CS) has been applied to MIMO radar imaging with limited antenna arrays. However, CS has to convert the matrix into a vector, which results in a large measurement matrix and a demanding amount of storage. Matrix completion (MC) is a new theory of recovering the entire data with partial observations. Compared with CS, MC is more suitable for matrix operations and avoids the basis mismatch problem. Due to the potential advantages, we introduce MC to MIMO radar imaging using a sparse planar array. Despite the success of MC in other fields, direct application in MIMO radar imaging leads to several problems, such as strict restrictions of the low-rank property and uniform random samples in MC. To solve these problems, a structured MC(StMC)-based method is proposed. With the method, MIMO radar imaging via a sparse planar array is converted to complete a low-rank structured matrix, which can be easily solved as a semi-definite program problem. Experimental results confirm the validity of the proposed method. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Multiple-input multiple-output (MIMO) radar Radar imaging sparse planar array Matrix completion (MC)

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Adaptive Beamforming for Cascaded sparse Diversely Polarized planar array

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第12期72卷 15648-15664页

作者： Yue, Yaxing Zhou, Chengwei Xing, Fangyuan Choo, Kim-Kwang Raymond Shi, Zhiguo Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Key Lab Collaborat Sensing & Autonomous Unmanned S Hangzhou 310015 Peoples R China Southeast Univ Sch Cyber Sci Engn Nanjing 211189 Peoples R China Univ Texas San Antonio Dept Informat Syst & Cyber Secur San Antonio TX 78249 USA Zhejiang Univ Int Joint Innovat Ctr Haining 314400 Peoples R China

Adaptive beamforming plays an increasingly vital role in various applications, including radar, sonar, telecommunications, and many other related fields. In this paper, we present an adaptive beamformer (called polarimetric sparse-Reconstruction beamformer) based on cascaded sparse diversely polarized planar array. The cascaded sparse array consists of multiple parallel sparse linear subarrays, whose difference co-array has no holes. The beamformer is operated in the joint spatial and polarization domain;thus, can potentially suppress the interferences that are close to the desired signal with different polarization states. Two key building blocks of our proposed beamformer are polynomial rooting-based closed-form two-dimensional direction-of-arrival and polarization joint estimation procedures, where the involved polynomial coefficients are also derived. Then, the closed-form power distribution estimation is presented to reconstruct the interference-plus-noise covariance matrix by leveraging the sparsity of the desired signal and interferences in the joint spatial and polarization domain. Thus, the proposed adaptive beamformer is computationally efficient, since all the procedures involved are formatted by closed-form expressions. Finally, a set of simulation examples are given to evaluate the characteristics of the proposed cascaded sparse array as well as the performance and robustness of the proposed beamformer in different scenarios, and compared against several existing approaches.

关键词： Adaptive beamforming closed-form direction of-arrival diversely polarized reconstruction sparse planar array

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Go-Caterpillar Mutation and Its Optimization Algorithm for Synthesis of Large-Scale sparse planar arrays

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2023年第3期71卷 2194-2207页

作者： Zhao, Heng Ban, Yong-Ling Liu, Yanhui Hu, Jun Nie, Zaiping Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu 611731 Peoples R China

Optimizing the layout of sparse planar arrays constrained by minimum element spacing to reduce the peak sidelobe level (PSLL) is a difficult and challenging task in engineering applications. Here, a new sparse array design method is proposed under the constraints of aperture size, the number of array elements, and minimum spacing between elements. The approach is based on a new element mutation method which is proposed for mutating the position of any element within the aperture without changing the position of other elements. Because a mutating element can be thought of as being placed inside the board like a black/white stone in go or crawling somewhere nearby like a caterpillar, we call it go-caterpillar-mutation (GCM). Based on GCM, a stochastic optimization algorithm (GCM-OA) is proposed to optimize the layout of sparse planar arrays. Several examples demonstrate the robustness and rapidity of GCM-OA in reducing PSLL by adjusting the array element positions under various constraints.

关键词： Finite element analysis Apertures planar arrays Layout Stochastic processes Encoding Antenna radiation patterns Antenna array go-caterpillar-mutation (GCM) optimization algorithm position perturbation model (PPM) sparse planar array

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