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

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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Subspace extension algorithm for 2D DOA estimation with L-shaped sparse array

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MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2017年第1期28卷 315-327页

作者： Liu, Sheng Yang, Lisheng Li, Dong Cao, Hailin Chongqing Univ Key Lab Aerocraft Tracking Telemetering Command & Chongqing 400044 Peoples R China

A subspace extension algorithm for two-dimensional (2D) direction-of-arrival (DOA) estimation with an L-shaped array is proposed. This L-shaped array is comprised of two orthogonal sparse linear arrays (SLAs). Each SLA consists of two different uniform linear arrays. The cross-correlation matrix of received data is used to construct two extended signal subspaces, by which the azimuth angles and elevation angles can be estimated independently. The procedure used to extend signal subspace only needs a small amount of calculation. Then, an effective pair-matching method is addressed to pair the estimated elevation angles and azimuth angles. Although the signal subspaces are extended, the complexity of the proposed 2D DOA estimation algorithm is lower than many similar algorithms. Simulation results indicate the availability of the proposed pairing-matching method and subspace extension algorithm.

关键词： Subspace extension DOA estimation sparse linear array Cross-correlation matrix Pairing-matching

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2D DOA estimation for noncircular sources using L-shaped sparse array

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MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2018年第2期29卷 489-502页

作者： Liu, Sheng Yang, Lisheng Li, Dong Cao, Hailin Jiang, Qingping Chongqing Univ Key Lab Aerocraft Tracking Telemetering Command & Chongqing 400044 Peoples R China

We present an algorithm for two-dimensional (2D) direction-of-arrival (DOA) estimation of noncircular sources using an L-shaped sparse array. An L-shaped sparse array consisting of two co-prime arrays is firstly introduced. Then, the fourth-order-cumulants (FOCs) of received data are used to construct a FOC matrix (FOCM), by which we can get the estimations of elevation angles. With the estimated elevation angles, the azimuth angles can be estimated by a low-complexity signal separation algorithm. During the procedure used for estimating azimuth angles, no any eigenvalue decomposition (EVD), peak search and pair-matching procedure need to be implemented. Although the aperture is extended significantly, the computation complexity of proposed algorithm still is acceptable. Compared with some analogous algorithms, our approach shows more attractive estimation performance. A lot of simulation results prove the advantages of proposed DOA estimation technology.

关键词： Direction-of-arrival estimation Non-circular signal sparse linear array Fourth-order-cumulant

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Resolving manifold ambiguities for direction-of-arrival estimation of sparse array using semi-circular substrates

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IET MICROWAVES ANTENNAS & PROPAGATION 2013年第12期7卷 1016-1020页

作者： Liu, Jiazhou Zhao, Zhiqin He, Ziyuan Nie, Zaiping Liu, Qing-Huo Univ Elect Sci & Technol China Sch Elect Engn Chengdu 610054 Peoples R China Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA

sparse linear array has some advantages over uniform linear array in some applications. However, it is well known that sparse array always suffers from manifold ambiguity, which has significant influence on direction-of-arrival (DOA) estimation and resolution. In this study, a new method based on the multiple signal classification (MUSIC) for resolving manifold ambiguities of uncorrelated sources by using semi-circular substrate is proposed for DOA estimation of sparse array. Spurious MUSIC peak spectrums are generated because of a linear combination of the steering vectors of true DOAs. The main idea of the proposed method is to setup one or several semi-circular medium substrates at the front of some elements. The phases of these elements are changed through the refractive index and the radii of those semi-circular substrates. This treatment breaks down the previous linearity of the steering vectors for the array without adding the substrates. Thus, true DOA peaks can be discriminated according to this feature. Simulation results show good performance of the proposed method. Trivial and non-trivial ambiguities are efficiently resolved.

关键词： array signal processing direction-of-arrival estimation refractive index signal classification direction-of-arrival estimation sparse linear array uniform linear array manifold ambiguity DOA estimation DOA resolution multiple-signal classification uncorrelated sources spurious MUSIC peak spectrums linear combination steering vector linearity semicircular medium substrates refractive index semicircular substrate radii

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Synthesis of sparse linear arrays With Reduced Excitation Control Numbers Using a Hybrid Cuckoo Search Algorithm With Convex Programming

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2020年第3期19卷 428-432页

作者： Wang, Rui-Qi Jiao, Yong-Chang Xidian Univ Natl Key Lab Antennas & Microwave Technol Xian 710071 Peoples R China

sparse linear arrays based on a novel subarrayed scheme are proposed and synthesized in this letter. The array with a fixed aperture size is partitioned into several uniformly spaced subarrays while number, spacing, and excitation in each subarray are optimized with multiple constraints. Compared with conventional sparse linear array with all the elements excited independently, the sparse linear array with the novel subarrayed scheme provides excitations at the subarray port and reduces the excitation control numbers remarkably. By integrating the cuckoo search (CS) algorithm with convex programming (CP), a hybrid CS-CP method is proposed and applied to the synthesis problem while the constraints are satisfied during the optimization process. Three examples with series of cases are presented, and the obtained results are compared to those presented in some state-of-the-art references. The optimized array achieves an improved peak sidelobe level and reduced excitation control number.

关键词： Convex programming (CP) excitation control number (ECN) multiple constraints peak sidelobe level (PSLL) sparse linear array subarrays

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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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sparse array Design for Distributed Aperture Coherence-Synthetic Radar

Sparse Array Design for Distributed Aperture Coherence-Synth...

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CIE International Conference on Radar (RADAR)

作者： Yu, Xianxiang Cui, Guolong Gao, Shangwei Kong, Lingjiang Zhang, Tianxian Yang, Jianyu Univ Elect Sci & Technol China Chengdu Peoples R China Sci & Technol Elect Informat Control Lab Chengdu Peoples R China

ISBN: (纸本)9781509048281

This paper considers the sparse linear array (SLA) design problem of coherent unambiguous transmit for the distributed aperture coherence-synthetic radar (DACR), accounting for the antenna size constraint and geography constraint in which elements can not be placed in a given aperture. We develop the peak sidelobe level (PSL) based cost function, which considers the sum of the PSL when the mainlobe located at all scanning angles. Then, we derive the antenna element locations by minimizing the cost function through PSO algorithm. Finally, several numerical simulations verify the effectiveness of the proposed SLA design method.

关键词： sparse linear array Distributed Aperture Coherence-synthetic Radar Antenna Size and Geography Constraints Peak Sidelobe Level

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Performance bounds for modal analysis using sparse linear arrays 6

Performance bounds for modal analysis using sparse linear ar...

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Conference on Compressive Sensing VI - From Diverse Modalities to Big Data Analytics

作者： Li, Yuanxin Pezeshki, Ali Scharf, Louis L. Chi, Yuejie Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA Colorado State Univ Dept Elect & Comp Engn Ft Collins CO 80523 USA Colorado State Univ Dept Math Ft Collins CO 80523 USA

ISBN: (数字)9781510609242

ISBN: (纸本)9781510609235;9781510609242

We study the performance of modal analysis using sparse linear arrays (SLAs) such as nested and co-prime arrays, in both first-order and second-order measurement models. We treat SLAs as constructed from a subset of sensors in a dense uniform linear array (ULA), and characterize the performance loss of SLAs with respect to the ULA due to using much fewer sensors. In particular, we claim that, provided the same aperture, in order to achieve comparable performance in terms of Cramer-Rao bound (CRB) for modal analysis, SLAs require more snapshots, of which the number is about the number of snapshots used by ULA times the compression ratio in the number of sensors. This is shown analytically for the case with one undamped mode, as well as empirically via extensive numerical experiments for more complex scenarios. Moreover, the misspecified CRB proposed by Richmond and Horowitz is also studied, where SLAs suffer more performance loss than their ULA counterpart.

关键词： sparse linear array Cramer-Rao bound compression ratio misspecification

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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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