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Imaging method for downward-looking sparse linear array three-dimensional synthetic aperture radar based on reweighted atomic norm

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JOURNAL OF APPLIED REMOTE SENSING 2016年第1期10卷

作者： Bao, Qian Han, Kuoye Lin, Yun Zhang, Bingchen Liu, Jianguo Hong, Wen Chinese Acad Sci Inst Elect Sci & Technol Microwave Imaging Lab North 4th Ring Rd West Beijing 100190 Peoples R China Technol Grp Corp Informat Sci Acad China Elect Informat Syst Technol Inst Beijing 100086 Peoples R China Univ London Imperial Coll Sci Technol & Med Dept Earth Sci & Engn South Kensington Campus London SW7 2AZ England

We propose an imaging algorithm for downward-looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) in the circumstance of cross-track sparse and nonuniform array configuration. Considering the off-grid effect and the resolution improvement, the algorithm combines pseudo-polar formatting algorithm, reweighed atomic norm minimization (RANM), and a parametric relaxation-based cyclic approach (RELAX) to improve the imaging performance with a reduced number of array antennas. RANM is employed in the cross-track imaging after pseudo-polar formatting the DLSLA 3-D SAR echo signal, then the reconstructed results are refined by RELAX. By taking advantage of the reweighted scheme, RANM can improve the resolution of the atomic norm minimization, and outperforms discretized compressive sensing schemes that suffer from off-grid effect. The simulated and real data experiments of DLSLA 3-D SAR verify the performance of the proposed algorithm. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： three-dimensional synthetic aperture radar sparse linear array reweighted atomic norm minimization off-grid effect pseudo-polar formatting algorithm RELAX

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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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2-D DOA Estimation Based on sparse linear arrays Exploiting Arbitrary linear Motion

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第9期73卷 13248-13262页

作者： Zhang, Zexiang Shen, Qing Liu, Wei Cui, Wei Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

In direction of arrival (DOA) estimation exploiting array motions, conventional designs consider sparse arrays moving along or perpendicular to the array direction, and only the second-order statistics are exploited. As the estimation performance is highly related to the synthetic virtual structure and source distribution, moving direction will definitely influence the performance. In this paper, we propose a generalized array synthesis method with a sparse linear array moving towards an arbitrary direction based on high-order difference co-arrays, where the physical array and moving sampling motions are specifically designed with improved degrees of freedom (DOFs). Unambiguity property of this arbitrary moving model is analyzed, followed by an affine transformation based unitary ESPRIT method for 2-D DOA estimation. The Cram & eacute;r-Rao bound is derived and an iterative moving direction optimization method is then proposed for further performance improvement. Simulation results are provided to verify the superior performance of the synthetic model, showing the impact of moving direction on the performance, and a better performance is achieved by optimizing the moving direction iteratively.

关键词： Estimation Direction-of-arrival estimation Sensor arrays Sensors Vectors Planar arrays Iterative methods 2-D DOA estimation high-order difference co-array moving platform sparse linear array synthetic aperture

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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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ON UNIQUE LOCALIZATION OF UNCORRELATED CONSTANT-MODULUS SOURCES USING sparse linear arrayS 49

ON UNIQUE LOCALIZATION OF UNCORRELATED CONSTANT-MODULUS SOUR...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wang, Wenlong Yang, Zai Wu, Xunmeng Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China

ISBN: (纸本)9798350344868;9798350344851

In direction-of-arrival (DOA) estimation, both stochastic and deterministic priors of source signals have been used to localize more sources than sensors. The stochastic prior refers to uncorrelateness of sources, while the deterministic one includes the constant modulus (CM) property of sources. Existing studies typically exploit one of the two priors. In this paper, we consider the combination of the two priors and present a necessary condition for unique localization of uncorrelated CM sources using a sparse linear array. Our result indicates limited benefits of exploiting both uncorrelated and CM priors in further improving the number of locatable sources.

关键词： Unique localization uncorrelated sources constant modulus sources sparse linear array

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IHT-INSPIRED NEURAL NETWORK FOR SINGLE-SNAPSHOT DOA ESTIMATION WITH sparse linear arrayS 49

IHT-INSPIRED NEURAL NETWORK FOR SINGLE-SNAPSHOT DOA ESTIMATI...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hu, Yunqiao Sun, Shunqiao Univ Alabama Dept Elect & Comp Engn Tuscaloosa AL 35401 USA

ISBN: (纸本)9798350344868;9798350344851

Single-snapshot direction-of-arrival (DOA) estimation using sparse linear arrays (SLAs) has gained significant attention in the field of automotive MIMO radars. This is due to the dynamic nature of automotive settings, where multiple snapshots aren't accessible, and the importance of minimizing hardware costs. Low-rank Hankel matrix completion has been proposed to interpolate the missing elements in SLAs. However, the solvers of matrix completion, such as iterative hard thresholding (IHT), heavily rely on expert knowledge of hyperparameter tuning and lack task-specificity. Besides, IHT involves truncated-singular value decomposition (t-SVD), which has a high computational cost in each iteration. In this paper, we propose an IHT-inspired neural network for single-snapshot DOA estimation with SLAs, termed IHT-Net. We utilize a recurrent neural network structure to parameterize the IHT algorithm. Additionally, we integrate shallow-layer autoencoders to replace t-SVD, reducing computational overhead while generating a novel optimizer through supervised learning. IHT-Net maintains strong interpretability as its network layer operations align with the iterations of the IHT algorithm. The learned optimizer exhibits fast convergence and higher accuracy in the full array signal reconstruction followed by single-snapshot DOA estimation. Numerical results validate the effectiveness of the proposed method.

关键词： sparse linear array matrix completion iterative hard thresholding deep neural networks single snapshot direction-of-arrival estimation

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Automotive Radar Sensing with sparse linear arrays Using One-Bit Hankel Matrix Completion

Automotive Radar Sensing with Sparse Linear Arrays Using One...

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IEEE Radar Conference (RadarConf)

作者： Eamaz, Arian Yeganegi, Farhang Hu, Yunqiao Sun, Shunqiao Soltanalian, Mojtaba Univ Illinois ECE Dept Chicago IL 60607 USA Univ Alabama Dept Elect & Comp Engn Tuscaloosa AL 35487 USA

ISBN: (纸本)9798350329216;9798350329209

The design of sparse linear arrays has proven instrumental in the implementation of cost-effective and efficient automotive radar systems for high-resolution imaging. This paper investigates the impact of coarse quantization on measurements obtained from such arrays. To recover azimuth angles from quantized measurements, we leverage the low-rank properties of the constructed Hankel matrix. In particular, by addressing the one-bit Hankel matrix completion problem through a developed singular value thresholding algorithm, our proposed approach accurately estimates the azimuth angles of interest. We provide comprehensive insights into recovery performance and the required number of one-bit samples. The effectiveness of our proposed scheme is underscored by numerical results, demonstrating successful reconstruction using only one-bit data.

关键词： Coarse quantization dithered one-bit sensing one-bit Hankel matrix completion sparse linear array singular value thresholding

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sparse array DESIGN FOR MIMO RADAR IN MULTIPATH SCENARIOS 49

SPARSE ARRAY DESIGN FOR MIMO RADAR IN MULTIPATH SCENARIOS

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Li, Xuchen Lin, Ronghao So, Hing Cheung Univ Sci & Technol China Dept EEIS Hefei Anhui Peoples R China City Univ Hong Kong Dept EE Hong Kong Peoples R China

ISBN: (纸本)9798350374520;9798350374513

sparse array designs have focused mostly on angular resolution (mainlobe width), peak sidelobe level and directivity factor of virtual arrays for multiple-input multiple-output (MIMO) radar. The notion of the MIMO radar virtual array is based on the direct path assumption in that the direction-of-departure (DOD) and direction-of-arrival (DOA) of the targets are equal. However, the DOD and DOA of targets in multipath scenarios are likely to be very different. The identification of multipath targets requires DOD-DOA imaging using the transmit and receive arrays, not the virtual array. To improve the imaging of both direct path and multipath targets, we introduce several new criteria for MIMO radar sparse linear array (SLA) designs for multipath scenarios. Under the new criteria, we adopt a cyclic optimization strategy under a coordinate descent framework to design the MIMO SLAs. We present several numerical examples to demonstrate the effectiveness of the proposed approaches.

关键词： array design multiple-input multiple-output radar sparse linear array multipath coordinate descent

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DOWNWARD LOOKING sparse linear array 3D SAR IMAGING ALGORITHM BASED ON BACK-PROJECTION AND CONVEX OPTIMIZATION

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Journal of Electronics(China) 2014年第4期31卷 298-309页

作者： Bao Qian Peng Xueming Wang Yanping Tan Weixian Hong Wen National Key Laboratory of Science and Technology on Microwave Imaging Institute of Electronics Chinese Academy of Sciences University of Chinese Academy of Sciences

Downward Looking sparse linear array Three Dimensional SAR(DLSLA 3D SAR) is an important form of 3D SAR imaging, which has a widespread application field. Since its practical equivalent phase centers are usually distributed sparsely and nonuniformly, traditional 3D SAR algorithms suffer from low resolution and high sidelobes in cross-track dimension. To deal with this problem, this paper introduces a method based on back-projection and convex optimization to achieve 3D high accuracy imaging reconstruction. Compared with traditional SAR algorithms, the proposed method sufficiently utilizes the sparsity of the 3D SAR imaging scene and can achieve lower sidelobes and higher resolution in cross-track dimension. In the simulated experiments, the reconstructed results of both simple and complex imaging scene verify that the proposed method outperforms 3D back-projection algorithm and shows satisfying cross-track dimensional resolution and good robustness to noise.

关键词： Three Dimensional SAR （3D SAR） Downward looking sparse linear array Convex optimizationCLC number：TN957

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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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