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Direction-of-Arrival Estimation for a Random sparse linear array Based on a Graph Neural Network

SENSORS 2024年第1期24卷 91页

作者： Yang, Yiye Zhang, Miao Peng, Shihua Ye, Mingkun Zhang, Yixiong Xiamen Univ Sch Elect Sci & Technol Xiamen 361005 Peoples R China Tung Thih Electron Res Ctr Xiamen 361021 Peoples R China

This article proposes a direction-of-arrival (DOA) estimation algorithm for a random sparse linear array based on a novel graph neural network (GNN). Unlike convolutional layers and fully connected layers, which do not interact well with information between different antennas, the GNN model can adapt to the goniometry problem of non-uniform random sparse linear arrays without any prior information by applying neighbor nodes' aggregation and update operations. This helps the model in learning signal features under complex environmental conditions. We train the model in an end-to-end way to reduce the complexity of the network. Experiments are conducted on the uniform and sparse linear arrays for various signal-to-noise ratio (SNR) and numbers of snapshots for comparison. We prove that the GNN model has superior angle estimation performance on arrays with large sparsity that cannot be used by traditional algorithms and surpasses existing deep learning models based on convolutional or fully connected structures. The proposed algorithm shows excellent DOA estimation performance under the complex conditions of limited snapshots, low signal-to-noise ratio, and large array sparsity as well. In addition, the algorithm has a low time calculation cost and is suitable for scenarios that require low latency.

关键词： direction-of-arrival (DOA) estimation sparse linear array graph neural network single snapshot array signal processing

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Optimal sparse linear array design with reduced mutual coupling effect

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2023年 170卷

作者： Ebrahimi, Mohammad Karimi, Mahmood Modarres-Hashemi, Mahmoud Isfahan Univ Technol Esfahan 84156 Iran Shiraz Univ Shiraz Iran

sparse linear arrays (SLAs) provide a high number of degrees of freedom (DOF) and reduce the mutual coupling effect between sensors of the array. Many design methods for SLAs have been proposed in the last two decades but most of the existing SLA design methods fail to achieve minimum number of sensors for a desired DOF. In this paper, a design method is proposed which utilizes the branch and bound (B & B) optimization algorithm to give a SLA with the desired DOF and minimum number of physical sensors. The proposed SLA design method is obtained by generalizing the minimum sensor array (MSA) design method which is recently proposed in the literature. This generalized method provides the same DOF as the MSA method but with a smaller number of physical sensors. In addition, a version of this design method is proposed which provides more robustness against mutual coupling effect between the sensors of the array. Simulation results demonstrate the superiority of the performance of the SLAs obtained with the proposed method over the arrays obtained with the MSA method and also over the other existing SLAs.

关键词： sparse linear array Mutual coupling effect Hole free array linear integer programming (LIP)

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A New Fitness Function for sparse linear array Evaluation Based on the Point Spread Function

A New Fitness Function for Sparse Linear Array Evaluation Ba...

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IEEE UFFC Latin America Ultrasonics Symposium (LAUS)

作者： Eduardo de Souza, Julio Cesar Prado, Vander Teixeira Martinez-Graullera, Oscar Higuti, Ricardo Tokio Univ Estadual Paulista UNESP Dept Engn Elect Ilha Solteira Brazil Univ Tecno Fed Parana UTFPR Dept Engn Elect Cornelio Procopio Brazil CSIC Inst Tecno Fis & Informac Madrid Spain

ISBN: (纸本)9781665443593

This work proposes a new fitness function for sparse linear array design using the Point Spread Function (PSF), where the energy and entropy are combined. The Arithmetic Optimization Algorithm is used as a search mechanism, and a new strategy to find sparse linear configurations is proposed. This new strategy eliminates the necessity of penalization functions to control the number of elements in sparse arrays. The algorithm is used to find two sparse linear arrays, one using a fitness function based on the radiation pattern, and the other with the proposed fitness function. Comparing the results, the configuration found using the proposed fitness function presented better lateral resolution and contrast.

关键词： sparse linear array Fitness Function Ultrasound Imaging Arithmetic Optimization Algorithm

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Grid-Less DOA Estimation via sparse linear array Using One-Bit Measurements

Grid-Less DOA Estimation via Sparse Linear Array Using One-B...

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2021 CIE International Conference on Radar, Radar 2021

作者： Xia, Yu Xi, Feng Nanjing University of Science and Technology Department of Electronic Engineering Nanjing China

ISBN: (纸本)9781665498142

Recently one-bit quantization has been widely used in field of signal processing because of its advantages of low cost, low power consumption and high sampling rate. This paper studies the problem of estimating the direction of arrivals (DOAs) of source signals via sparse linear arrays (SLAs) using one-bit measurements. By constructing an augmented covariance matrix from the sample covariance matrix, we can realize gridless DOA estimation using root-MUSIC on the augmented covariance matrix and solve more sources than the number of sensors. Two covariance fitting criteria, including maximum likelihood and Wasserstein distance, are introduced into one-bit sparse array DOA estimation. Numerical simulations demonstrate that Wasserstein distance based criterion has good performance both in accuracy and efficiency. © 2021 IEEE.

关键词： direction of arrival (DOA) estimation one-bit quantization sparse and parametric approach (SPA) sparse linear array Wasserstein distance

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Gridless Direction of Arrival Estimation Exploiting sparse linear array

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IEEE SIGNAL PROCESSING LETTERS 2020年 27卷 1625-1629页

作者： Chen, Tao Shi, Lin Guo, Limin Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Harbin Engn Univ Minist Ind & Informat Technol Key Lab Adv Marine Commun & Informat Technol Harbin 150001 Peoples R China

In this letter, a dual formulation of atomic norm minimization (ANM) approach is proposed by exploiting the vectorized covariance data of the signals received by the extended virtual array. Compared with the traditional ANM-based gridless direction of arrival (DOA) estimation algorithms for sparse linear array (SLA), the proposed algorithm not only realizes the parameter continuity and data completion, but also reduces the size of optimization model and the influence of noise on the estimation accuracy, which can significantly improve the performance of the algorithm. In addition, after establishing an ANM model based on complete vectorized covariance data, the corresponding dual problem is formulated to make the subsequent DOA recovery process no longer need the number of sources as a priori information. Numerial simulations demonstrate the superiority of the proposed algorithm over state of the art gridless DOA estimation algorithms.

关键词： Direction-of-arrival estimation arrays Estimation Signal processing algorithms Covariance matrices Optimization array signal processing Gridless direction of arrival sparse linear array atomic norm minimization matrix completion

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A sparse linear array APPROACH IN AUTOMOTIVE RADARS USING MATRIX COMPLETION

A SPARSE LINEAR ARRAY APPROACH IN AUTOMOTIVE RADARS USING MA...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Sun, Shunqiao Petropulu, Athina P. Univ Alabama Dept Eeletr & Comp Engn Tuscaloosa AL 35487 USA Rutgers State Univ Dept Elect & Comp Engn Piscataway NJ 08854 USA

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066315

We consider an automotive radar using a sparse linear array (SLA) in the context of multi-input multi-output (MIMO) radar. The key problem in SLA is the selection of the locations of the array elements so that the peak sidelobe level of the virtual SLA beampattern is low. Prior approaches have focused on optimal sparse array design, or use of interpolation techniques for filling the holes in the synthesized SLA before applying digital beamforming for angle finding. In this paper, different from previous efforts, we use matrix completion to complete the corresponding virtual uniform linear array (ULA) before estimating the target angle. In particular, we show that for a small number of targets within the same range-Doppler cell, the Hankel matrix constructed by subarrays of the virtual ULA is low-rank, and thus under certain conditions, can be completed based on the SLA measurements. We derive the coherence properties of the Hankel matrix so that the matrix can be competed via nuclear norm minimization methods. We also demonstrate via examples the effect of various SLA topologies on the identifiability of the Hankel matrix.

关键词： automotive radar FMCW autonomous driving sparse linear array matrix completion

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Steerable sparse linear array Synthesis using Second Order Cone Programming

Steerable Sparse Linear Array Synthesis using Second Order C...

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IEEE Underwater Technology (UT) Conference

作者： Goel, Arun Kumar, Arun Bahl, Rajendar Indian Inst Technol Delhi Appl Res Elect New Delhi India

ISBN: (纸本)9781538641880

We present the design of steerable sparse linear array (SLA) for 2D forward looking sonar imaging system in this paper. The mathematical model includes constraints for multiple steering directions in the Field Of View (FOV) and this model is based on the Compressive Sensing (CS) theory. The sparsity in this model is introduced by considering oversampled linear aperture (i.e. densely populated aperture by sensors). This model defines the Main Lobe Width (MLW) constraints indirectly by matching the beam pattern of the synthesized SLA with the desired beam pattern in the main lobe region for all steering direction. The Side Lobe Peak (SLP) constraints in this model are defined directly for all steering directions. This optimization problem is a disciplined convex program and its equivalent formulation is a second order cone programming problem. The SLA synthesized using this method has separate weights for each steering directions. The SLA synthesized using this method is compared with the SLA designed using Multiple beam, Measurement Vectors (MMV) CS method. The performance of the synthesized SLAs is compared in terms of reduction of sensors with half wavelength spacing Uniform linear array (ULA) and MLW % error. We present the design results of forward looking sonar imaging system for frequencies 37.5 kHz and 75 kHz having angular resolution 6.4 degrees and 3.2 degrees respectively, with 11 and 19 beams in FOV.

关键词： Forward looking sonar imaging sparse linear array compressive sensing field of view

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Gridless sparsity-based DOA estimation for sparse linear array

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JOURNAL OF ENGINEERING-JOE 2019年第20期2019卷 6629-6632页

作者： Zhang, Yu Zhang, Gong Kong, Yingying Wen, Fangqing Nanjing Univ Aeronaut & Astronaut Key Lab Radar Imaging & Microwave Photon Minist Educ Nanjing Jiangsu Peoples R China Yangtze Univ Elect & Informat Sch Jingzhou Peoples R China

A novel two-stage gridless sparsity-based method for sparse linear array direction-of-arrival (DOA) estimation is presented. First, the covariance matrix gridless sparse representation method based on atomic norm minimisation is proposed for corresponding structured Toeplitz matrix construction and source number detection. Then, the conventional MUSIC algorithm can be employed for DOA estimation. Compared with conventional subspace-based algorithms, the proposed two-stage method can be carried out without knowing source number and directly detect more source signals than sensors. Numerical simulations demonstrate the effectiveness and outperformance of the proposed method.

关键词： Toeplitz matrices array signal processing eigenvalues and eigenfunctions covariance matrices direction-of-arrival estimation gridless sparsity-based DOA estimation sparse linear array two-stage gridless sparsity-based method array direction-of-arrival estimation covariance matrix gridless sparse representation method atomic norm minimisation corresponding structured Toeplitz matrix construction source number conventional MUSIC algorithm conventional subspace-based algorithms two-stage method

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Collision-interference cancellation for space-based AIS using beam synthesis technology with optimal sparse linear array

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INTERNATIONAL JOURNAL OF SATELLITE COMMUNICATIONS AND NETWORKING 2017年第2期35卷 109-122页

作者： Zha, Guofeng Wang, Hongqiang Cheng, Yongqiang Qin, Yuliang Natl Univ Def Technol Sch Elect Sci & Engn Changsha Hunan Peoples R China

A basic problem in the space-based automatic identification system (AIS) is the low probability of detecting messages because ships' messages arrive at the receiver in the same time slot. In this study, sparse linear array optimal beam synthesis (SLA-OBS) technology is proposed to improve the capture ability of AIS messages by forming a narrow beam pattern that points in the direction of the desired AIS messages. To capture the desired signal within the narrow beam pattern, the directions of arrival (DOA) and the number of sources from ships are first estimated. Then, the ideal narrow beam pattern and minimal number of array elements are achieved synchronously with the CPLEX optimal tool. The simulations show that the message detection probability with the proposed method is greater than 95%, even when the situations are very serious, whereas the number of sparse linear antennas is small (no more than six). Copyright (C) 2015 John Wiley & Sons, Ltd.

关键词： space-based AIS sparse linear array optimal beam synthesis technology DOA estimation

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Low Sidelobe Nulling of sparse linear array Using a Hybrid Method 6

Low Sidelobe Nulling of Sparse Linear Array Using a Hybrid M...

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6th IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP) / Asia-Pacific Electromagnetic Week (APEMW)

作者： Wang, Rui-Qi Jiao, Yong-Chang Xidian Univ Natl Key Lab Antenna & Microwave Technol Xian Shaanxi Peoples R China

ISBN: (纸本)9781538616086

This paper presents a hybrid method for the synthesis of sparse but symmetric linear array. Multiple constraints are taken into account for multiple objectives such as low sidelobe level (SLL), and nulling in specific directions. Two examples are presented and compared to those obtained by CLPSO, IWO/WDO and MCFO algorithms. Based on the comparisons, the proposed method gives a better suppression onto the sidelobe and interference.

关键词： Sidelobe suppression null control sparse linear array

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