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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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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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sparse linear Antenna array Design in Compact MIMO Radar using Genetic Algorithm 4

Sparse Linear Antenna Array Design in Compact MIMO Radar usi...

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4th International Conference on Information Science and Control Engineering (ICISCE)

作者： Han, Kuoye Jiang, Mian Wang, Huan Chen, Yanmin Liu, Guanghong China Elect Technol Grp Corp CETC Informat Sci Acad ISA Beijing 100098 Peoples R China

ISBN: (纸本)9781538630136

For compact multiple-input multiple-output (MIMO) active sensing applications, the issue of how to arrange the transmit and receive antenna elements in order to obtain a sparse linear array configuration design is considered. The principle of aperture synthesis is presented and a modified genetic optimizing algorithm is used with the fitness function being constructed. At last, Numerical results are also presented, which demonstrate the proposed design method.

关键词： sparse linear array minimum redundancy array multiple-input multiple-output (MIMO) radar genetic algorithm

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A Third-Order sparse array Design Scheme Based on Second-Order Sum-Difference Co-array Analysis

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第4期74卷 6108-6120页

作者： Guo, Haodong Chen, Hua Yang, Songjie Liu, Wei Yuen, Chau So, Hing Cheung Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Univ Elect Sci & Technol China Natl Key Lab Wireless Commun Chengdu 610054 Peoples R China Hong Kong Polytech Univ Dept Elect & Elect Engn Hong Kong Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore City 639798 Singapore Univ Hong Kong Dept Elect Engn City SAR Hong Kong Peoples R China

Recently, non-conventional odd-order statistics, especially the third-order cumulants (TOCs), have been introduced to direction-of-arrival (DOA) estimation based on third-order nested array (TONA). Compared with the methods employing second-order statistics, it significantly increases the number of uniform degrees-of-freedom (uDOFs), which can even be comparable to the fourth-order cumulant approach with a small number of array elements. In this paper, through analysis of the second-order sum-difference co-array, a third-order sparse linear array (SLA) design scheme suitable for TOCs is proposed, which can extend the commonly used second-order SLAs to the third-order, and prototype arrays are analyzed along with their closed-form expressions. It is shown that by selecting an appropriate generator array, the derived third-order SLA has far more uDOFs than the existing TONA, and also reduces the mutual coupling effect. In addition, a strategy is developed to mitigate the mutual coupling effect further by mirror-flipping some elements of the dense segment. The proposed array design scheme balances high uDOFs and low mutual coupling, greatly improving its robustness as well as DOA estimation accuracy.

关键词： Mutual coupling Vectors Sensor arrays Estimation Prototypes Direction-of-arrival estimation Manifolds Matrix converters Closed-form solutions Accuracy DOA estimation mutual coupling sparse linear array sum-difference co-array third-order cumulant (TOC)

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Prediction-driven Untrained Network for Single-snapshot sparse array Interpolation

Prediction-driven Untrained Network for Single-snapshot Spar...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Bu, Yinyan Yu, Jiajie Pal, Piya Department of ECE University of California La Jolla San Diego United States

ISBN: (纸本)9798350368741

sparse linear arrays (SLAs) have gained significant attention for automotive radar due to their enhanced aperture and angular resolution. A promising approach to Direction-of-Arrival (DOA) estimation with only a single temporal snapshot and SLAs is to interpolate the missing measurements by solving a low-rank Hankel matrix completion problem. Sampling geometry plays an important role in matrix completion, and random sampling techniques primarily provide non-uniform recovery guarantees, which can be undesirable. Using the unique geometry of nested-type SLAs, we formulate the Hankel matrix completion problem as a non-convex prediction problem which utilizes the latent structures of a low-rank Hankel matrix in order to reduce the number of unknowns. It also provides uniform recovery guarantees (provably in the absence of noise). Our approach can be interpreted as a self-supervised Untrained Neural Network (UNN) which utilizes the inner uniform linear array (ULA) of a nested array to perform prediction, and the outer array to curb error propagation. Our work establishes the first connection between array interpolation and untrained machine learning paradigms. © 2025 IEEE.

关键词： array Interpolation Direction-of-arrival Estimation Matrix Completion sparse linear array Untrained Networks

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Gridless DOA Estimation with Extended array Aperture in Automotive Radar Applications

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REMOTE SENSING 2025年第1期17卷 33-33页

作者： Jiang, Pengyu Gao, Silin Zhao, Jie Zhang, Zhe Zhang, Bingchen Chinese Acad Sci Key Lab Technol Geospatial Informat Proc & Applica Beijing 100190 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Beijing Autord Tech Co Ltd Beijing 100012 Peoples R China Suzhou Key Lab Microwave Imaging Proc & Applicat T Suzhou 215000 Peoples R China Suzhou Aerosp Informat Res Inst Suzhou 215000 Peoples R China Natl Key Lab Microwave Imaging Technol Beijing 100190 Peoples R China

Millimeter-wave automotive radar has become an essential tool for autonomous driving, providing reliable sensing capabilities under various environmental conditions. To reduce hardware size and cost, sparse arrays are widely employed in automotive radar systems. Additionally, because the targets detected by automotive radar typically exhibit sparsity, compressed sensing-based algorithms have been utilized for sparse array reconstruction, achieving superior performance. However, traditional compressed sensing algorithms generally assume that targets are located on a finite set of grid points and perform sparse reconstruction based on predefined grids. When targets are off-grid, significant off-grid errors can occur. To address this issue, we propose an automotive radar sparse reconstruction algorithm based on accelerated Atomic Norm Minimization (ANM). By using the Iterative Vandermonde Decomposition and Shrinkage Threshold (IVDST) algorithm, we can achieve fast ANM, which effectively mitigates off-grid errors while reducing reconstruction complexity. Furthermore, we adopt a Generalized Likelihood Ratio Test (GLRT) detector to eliminate noise and clutter in the automotive radar operating environment. Simulation results show that our proposed algorithm significantly improves reconstruction accuracy compared to the iterative soft threshold (IST) algorithm while maintaining the same computational complexity. The effectiveness of the proposed algorithm in practical applications is further validated through real-world data experiments, demonstrating its superior capability in clutter elimination.

关键词： millimeter-wave automotive radar sparse linear array difference co-array atomic norm minimization (ANM) GLRT detector

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Local-Do Fs of Airborne Radar Clutter for Reduced-Dimensional STAP with sparse Antenna array

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Chinese Journal of Electronics 2015年第2期24卷 426-433页

作者： ZHANG Zenghui YU Wenxian School of Electronic Science and Electrical Engineering Shanghai Jiao Tong University

Space-time adaptive processing(STAP)technique can mitigate airborne radar clutter and detect moving targets effectively. Based on adaptive radar theory, the Degrees-of-freedom(Do F) of a STAP processor should be larger than that of the received clutter. The local-Do Fs of clutter for Reduced-dimensional STAP(RDSTAP) methods with sparse linear array are studied. The proposed formulas show that the local-Do Fs of clutter can be estimated as the product of the equivalent clutter bandwidth with the equivalent sampling interval.

关键词： Local-Do Fs of clutter Space-time adaptive processing Reduced-dimension sparse linear array Clutter mitigation

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Low-Complexity MUSIC-Like Algorithm with sparse array

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WIRELESS PERSONAL COMMUNICATIONS 2016年第3期86卷 1265-1279页

作者： Liu, Sheng Yang, Lisheng Chen, Zhixiang Jiang, Qingping Chongqing Univ State Key Lab Aerocraft Tracking Telemetering Com Chongqing 400044 Peoples R China

This paper presents a low-complexity MUSIC-like algorithm with sparse linear array. Three uniform linear arrays are combined into a sparse linear array. An extended signal subspace is got by organizing the forth-order-cumulant of array received data. During this process, no eigen-value decomposition (EVD) or singular-value decomposition needs to be implemented. Then, a MUSIC-like method is proposed to estimate the direction-of-arrival of incident signals. In order to bring down the computational complexity further, an ESPRIT-like algorithm is used to obtain the initial estimations of direction angles, by which the search range can be diminished significantly. Compared with the classical MUSIC and PM, the proposed MUSIC-like algorithm shows better angular resolution and higher estimation accuracy. Moreover, because of the avoidance of EVD and the reduction of search range, the computational burden of the proposed MUSIC-like algorithm with per-estimation by ESPRIT-like algorithm is small. The performance of the proposed method is demonstrated through numerical simulations.

关键词： Direction-of-arrival MUSIC-like ESPRIT-like sparse linear array Forth-order-cumulant

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Joint sparsity-driven three-dimensional imaging method for multiple-input multiple-output radar with sparse antenna array

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IET RADAR SONAR AND NAVIGATION 2017年第5期11卷 709-720页

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

Three-dimensional (3D) imaging of a moving target can be achieved via a wideband multiple-input multiple-output (MIMO) radar with planar array in a single snapshot. The sparse recovery (SR) method has been introduced to deal with the MIMO imaging problem with sparse array recently. For sparse linear array, SR method can successfully achieve the high cross-range resolution. For sparse planar array, however, it is not effective to use a SR algorithm directly in 2D cross-range direction, because the final 2D imagery will be blurred by the SR-induced range cell migration (RCM) in the former cross-range direction. To remove the RCM, a joint sparsity-driven method is proposed in this study by exploiting the joint sparsity of echo signal. An improved imagery can be obtained with fewer antennas and higher calculation efficiency. Comparative experiments demonstrate the superiority of the proposed method.

关键词： MIMO radar planar antenna arrays radar antennas radar imaging radar resolution broadband antennas linear antenna arrays echo motion compensation joint sparsity-driven three-dimensional imaging method sparse antenna array 3D imaging moving target imaging wideband multiple-input multiple-output radar wideband MIMO radar sparse recovery method SR method MIMO imaging problem sparse linear array cross-range resolution sparse planar array 2D cross-range direction 2D imagery SR-induced range cell migration SR-induced RCM echo signal

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Explicit Ziv-Zakai bound for analysis of DOA estimation performance of sparse linear arrays

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SIGNAL PROCESSING 2013年第12期93卷 3449-3458页

作者： Khan, Diba Bell, Kristine L. George Mason Univ Dept Stat Fairfax VA 22030 USA

sparse linear arrays (SLAs) provide similar direction-of-arrival estimation performance to filled linear arrays in terms of angular accuracy and resolution with reduced size, weight, power consumption, and cost. However, they are subject to significant ambiguities due to high sidelobes in the array beampattern, which give rise to large estimation errors. In this paper, we develop an explicit closed-form expression for the Ziv-Zakai bound on the mean square estimation error in order to quantify the degradation in estimation performance due to the sidelobe ambiguities. The bound consists of three terms which correspond to the three types of estimation errors: small mainlobe errors, errors due to sidelobe ambiguities, and random errors. The bound is used to analyze the performance of different SLA configurations. Maximum likelihood estimation simulations confirm the contribution of the different types of estimation errors predicted by the bound. The analysis shows that much of the performance degradation due to ambiguities are from random errors that cannot be controlled by array design, while additional degradation due to sidelobe errors depends strongly on the array configuration. Isolating the contributions of the three types of errors provides greater understanding of the behavior of sparse arrays, allowing for more effective system design and analysis. (C) 2013 Elsevier B.V. All rights reserved.

关键词： DOA estimation sparse linear array Ziv-Zakai bound

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