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14th European Conference on Antennas and Propagation (EuCAP)

作者： Yang, Feng Yang, Shiwen Chen, Yikai Rocca, Paolo Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu Peoples R China Univ Trento ELEDIA Res Ctr Trento Italy

ISBN: (数字)9788831299008

ISBN: (纸本)9788831299008

A hybrid l(1) minimization algorithm with enhanced sparsity is proposed for the synthesis of sparse linear arrays. Moreover, the desired directivity as a constraint condition for a fixed scanning range is included in the hybrid algorithm in order to obtain a better pattern. In particular, the proposed hybrid algorithm can be divided into two steps. The first step is to iteratively solve a smooth-weighted l(1) minimization problem. The obtained weight vector is then considered as the initial weight vector in the second step, where a weighted l(1) minimization problem is solved. In comparison to other methods, simulation results show that the proposed method has an improved synthesis efficiency for sparse arrays.

关键词： hybrid algorithm sparse linear array

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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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L-Shaped sparse array Structure for 2-D DOA Estimation

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IEEE ACCESS 2020年 8卷 140030-140037页

作者： Wu, Fan Cao, Fei Ni, Xiaogang Chen, Chong Zhang, Yanhong Xu, Jianfeng High Tech Inst Xian Xian 710025 Peoples R China Univ Sci & Technol Beijing USTB Sch Comp & Commun Engn Beijing 100083 Peoples R China

In order to improve the performance of two-dimensional (2-D) direction-of-arrival (DOA) estimation, an L-shaped sparse array structured by two new sparse linear arrays is proposed. Each part of the proposed L-shaped array is used for one-dimensional (1-D) azimuth and elevation estimation, respectively. The new sparse linear array configuration is consisted of two subarrays which have N and M physical sensors, respectively. Owing to the advantages of the proposed sparse linear array configuration, higher degrees-of-freedom (DOF) and larger array aperture can be achieved when the second-order statistics of the received data is used. To match the azimuth and elevation angles automatically, the cross-covariance matrix of the two parts of the proposed L-shaped array is used to estimate the paired DOA angles. Based on the proposed L-shaped array configuration, (M + 1)(N - [M/2]) signal directions can be estimated with M CN sensors in each part and totally 2M + 2N + 1 sensors. Through the comparative analysis of the parameters with other sparse planar arrays, the proposed L-shaped array can achieve better performance due to its higher DOF and larger array aperture. Finally, numerical simulation results verify the superiority of the 2-D DOA method based on the proposed L-shaped array.

关键词： Degrees-of-freedom direction-of-arrival estimation L-shaped arrays sparse linear array

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DOA estimation using sparse array with gain-phase error based on a novel atomic norm

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DIGITAL SIGNAL PROCESSING 2022年 120卷 103266-103266页

作者： Gong, Qishu Ren, Shiwei Zhong, Shunan Wang, Weijiang Beijing Inst Technol Sch Informat & Elect 5 South Zhongguancun St Beijing Peoples R China

sparse arrays have attracted great attention in the field of direction-of-arrival (DOA) estimation due to the extended degrees of freedom (DOFs). Nevertheless, the traditional DOA estimation methods for sparse arrays suffer from degraded performance when sensor elements are uncalibrated. This paper presents a novel atomic norm-based algorithm for source localization with arbitrary sparse linear array (SLA) in the scenario with gain-phase uncertainties. Our proposed approach defines a new atomic norm for second order virtual signal by taking model errors into consideration. Then, the dual problem corresponding to original optimization problem is formulated to recover the DOAs by defining the dual atomic norm. We further present the corresponding semidefinite program characteristic that can be solved. The proposed method avoids iterations and restrictions on array configuration. It makes full use of all the DOFs provided by difference coarray of arbitrary SLA to estimate more sources and to provide high accuracy. Besides, compared with the existing coarray-based calibrated algorithms, the proposed algorithm does not need discretization on spatial domain. Computer simulations are carried out to demonstrate the superiority of the proposed algorithm. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Atomic norm Direction-of-arrival estimation Gain-phase uncertainties Semidefinite program sparse linear array

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Hole-Free Symmetric Complementary sparse array Design for High-Precision DOA Estimation

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REMOTE SENSING 2024年第24期16卷 4711-4711页

作者： Ma, He Liu, Libao Gan, Zhihong Gao, Yang Mao, Xingpeng Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China Minist Ind & Informat Technol Key Lab Marine Environm Monitoring & Informat Proc Harbin 150001 Peoples R China Harbin Inst Technol Sch Sci Weihai 264209 Peoples R China

Direction of arrival (DOA) estimation plays a critical role in remote sensing, where it aids in identifying and tracking multiple targets across complex environments, from atmospheric monitoring to resource mapping. Leveraging difference covariance array (DCA) for DOA estimation has become prevalent, particularly with sparse arrays capable of resolving more targets than the number of sensors. This paper proposes a new hole-free sparse array configuration for remote sensing applications to achieve improved DOA estimation performance using DCA. By symmetrically placing a minimum redundancy array (MRA) and its complementary MRA on both sides of a sparse uniform linear array (ULA), this configuration maximizes degrees of freedom (DOFs) and minimizes mutual coupling effects. Expressions for calculating sensor positions and optimal element allocation methods to maximize DOFs are derived. Simulation experiments in various scenarios have shown the advantages of the proposed array in DOA estimation, including a strong ability to estimate multi-targets, high angular resolution, low estimation error, and strong robustness to mutual coupling.

关键词： DOA estimation degrees of freedom difference covariance array sparse linear array

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Fast and efficient DOA estimation method for signals with known waveforms using nonuniform linear arrays

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SIGNAL PROCESSING 2015年 114卷 265-276页

作者： Gu, Jian-Feng Zhu, Wei-Ping Swamy, M. N. S. Ecole Polytech Dept Elect Engn Montreal PQ H3C 3A7 Canada Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada

In this paper, a new approach is proposed to estimate the direction of arrival (DOA) of multiple non-coherent source signals with known waveforms but unknown gains based on a nonuniform linear sensor array. Unlike some previous methods, which estimate the DOA using spatial signatures of the signals with known waveforms, the proposed method first uses the known waveforms and mutually independent sensor measurement noises to establish a maximum likelihood estimation problem corresponding to multiple linear regression models, each containing the DOA and the gain information of all the source signals. Then, regression analysis is performed to estimate the coefficients of each linear regression model, and the well-known generalized least squares is used to obtain the estimates of the angles and gains from the estimated regression coefficients. The proposed method does not require a search over a large region of the parameter space, which is normally needed in ML-based DOA estimation methods. The effect of correlated sources on the performance of the parameter estimation is also studied. It is shown that the DOA and gain estimates are asymptotically optimal as the sources, tend to be uncorrelated. Finally, simulation results that demonstrate the estimation performance of the proposed method are given. (C) 2015 Elsevier B.V. All rights reserved.

关键词： DOA estimation Known waveform sparse linear array Maximum likelihood problem Regression analysis

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Optimal Design of Nonredundant sparse arrays With Noninteger Distance Between Elements

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IEEE SENSORS JOURNAL 2023年第17期23卷 20012-20019页

作者： Hosseini, Seyed Mohammad Karimi, Mahmood Shiraz Univ Sch Elect & Comp Engn Shiraz *** Iran

Direction-of-arrival (DOA) estimation using a sparse array has recently attracted considerable interest as an approach for increasing the number of detectable sources and reducing the adverse effects of mutual coupling between array elements. Nonredundant arrays (NRAs) are among the most common sparse array configurations. Each element in a conventional NRA is usually located at an integer multiple of the half-wavelength. This article proposes a method for designing optimal NRAs (ONRAs), where array elements can be placed at locations which are integer multiples of a fraction of the half-wavelength. As there are a limited number of available ONRAs for a desired number of array elements, the proposed method increases the number of available ONRAs. In addition, as the proposed design method has more flexibility in selecting the places of array elements, this enables us to obtain arrays that have a larger minimum distance between array elements, and therefore, the NRAs obtained by the proposed method can improve DOA estimation accuracy in the presence of mutual coupling. In addition to the ONRAs with minimum array aperture, this article also examines ONRAs with the desired aperture (ONRAda), ONRAs with reduced mutual coupling (ONRAmc), and hybrid ONRAs (ONRAhybrid). Simulation results show that, in the presence of mutual coupling, the NRAs obtained by the proposed method can outperform existing NRAs in estimating source DOAs.

关键词： Sensor arrays Sensors Estimation Direction-of-arrival estimation Mutual coupling array signal processing Optimization Direction-of-arrival (DOA) estimation minimum hole array (MHA) mutual coupling optimized nonredundant array (NRA) sparse linear array

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Phase Error Correction in sparse linear MIMO Radar Based on the Equivalent Phase Center Principle

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REMOTE SENSING 2024年第19期16卷 3685页

作者： Shao, Wenyuan Hu, Jianmin Ji, Yicai Pan, Jun Fang, Guangyou Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Chinese Acad Sci Key Lab Electromagnet Radiat & Sensing Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Chinese Acad Sci GBA Branch Aerosp Informat Res Inst Guangzhou 510530 Peoples R China Guangdong Prov Key Lab Terahertz Quantum Electroma Guangzhou 510530 Peoples R China

Multiple-input multiple-output (MIMO) technology is widely used in the field of radar imaging. array sparse optimization reduces the hardware cost of MIMO radar, while virtual aperture and the equivalent phase center (EPC) principle simplify the radar signal model and reduce the computation and complexity of imaging algorithms. However, the application of sparse array structure and the EPC principle produces a non-negligible phase error, which affects the imaging quality. This paper simplifies the MIMO radar signal model based on the phase center approximation, analyzes the phase error generated by this method, and proposes an improved phase error correction method to solve the problem that the target cannot be well-focused at non-reference distance during imaging. In addition, this paper designs a sparse linear MIMO array with a periodic structure, which reduces the number of transmitting and receiving units, system complexity, and hardware costs. The proposed phase correction method was combined with the wavenumber domain algorithm to simulate and experiment on the designed antenna array, and good experimental results were obtained to verify the effectiveness of the proposed method.

关键词： multiple-input multiple-output (MIMO) radar imaging equivalent phase center (EPC) phase error correction sparse linear array

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Nonasymptotic Performance Analysis of Direct-Augmentation and Spatial-Smoothing ESPRIT for Localization of More Sources Than Sensors Using sparse arrays

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第6期59卷 9379-9389页

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

Direction augmentation (DA) and spatial smoothing (SS), followed by a subspace method such as ESPRIT or MUSIC, are two simple and successful approaches that enable localization of more uncorrelated sources than sensors with a proper sparse array. In this paper, we carry out nonasymptotic performance analyses of DA-ESPRIT and SS-ESPRIT in the practical finite-snapshot regime. We show that their absolute localization errors are bounded from above by C-1(L root)max{sigma 2,C2} with overwhelming probability, where L is the snapshot number, sigma(2) is the Gaussian noise power, and C-1,C-2 are constants independent of L and sigma(2), if and only if they can do exact source localization with infinitely many snapshots. We also show that their resolution increases with the snapshot number, without a substantial limit. Numerical results corroborating our analysis are provided.

关键词： Direction-of-arrival estimation Sensors Covariance matrices Sensor arrays Location awareness Estimation error Performance analysis DOA estimation direct-augmentation ESPRIT nonasymptotic performance analysis sparse linear array spatial-smoothing ESPRIT

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Third-Order Nested array: An Optimal Geometry for Third-Order Cumulants Based array Processing

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 2849-2862页

作者： Sharma, Umesh Agrawal, Monika Indian Inst Technol Delhi Ctr Appl Res Elect New Delhi 110016 India

Several sparse linear array structures have recently been proposed to enhance the identifiability of direction-of-arrival estimation by using second-order or higher even-order statistics under the co-array equivalence. This paper aims to explore non-conventional odd-order statistics, namely third-order cumulants, to design a sparse linear array under the co-array equivalence for enhancing the identifiability of a given sensors array. This paper presents a mathematical framework to devise a third-order exhaustive co-array equivalence associated with third-order cumulants of the data. A novel sparse linear array geometry, namely the third-order nested array, is proposed by maximizing the third-order exhaustive co-array's virtual uniform linear array aperture to enhance the identifiability of the given sensors array. The proposed third-order nested array offers a significantly higher virtual uniform linear array aperture than other existing similar sparse linear array structures designed using the second-order statistics of the data for any given number of physical sensors. In addition, the proposed third-order nested array offers a relatively higher virtual uniform linear array aperture even than that of a sparse linear array structure designed using the fourth-order statistics for the number of physical sensors less than equal to eight. The enhanced virtual uniform linear array aperture associated with the proposed third-order nested array increases the identifiability along with the accuracy of direction of arrival estimation for a given number of physical sensors. However, the proposed third-order nested array needs relatively more data than the second-order statistics based sparse linear array, as higher-order statistics based estimators entail a relatively higher variance.

关键词： Sensor arrays Sensors Apertures Direction-of-arrival estimation Estimation Geometry array signal processing Third-order cumulant non-uniform linear array sparse linear array sum and difference co-array underdetermined direction of arrival estimation

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