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Non-circular signals for nested array: sum-difference co-array and direction of arrival estimation algorithm

IET RADAR SONAR AND NAVIGATION 2020年第1期14卷 27-35页

作者： Wang Yunfei Shen Jinqing Zhang Xiaofei He Yi Dai Xiangrui Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Peoples R China Southeast Univ State Key Lab Millimeter Waves Nanjing 210096 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

nested array has aroused remarkable attention due to the capability to obtain both enhanced degrees of freedom (DOFs) and enlarged array aperture, whereas only difference co-array is employed. Here, the authors construct the sum co-array of nested array by utilising the non-circular (NC) characteristic and, subsequently, the sum-difference (SD) co-array is obtained by careful crafting of the sum co-array and the typical difference co-array, which doubles the resulting co-array aperture and, simultaneously, further increases the achievable DOF compared with the difference co-array of nested array. Moreover, to circumvent the time-consuming two-dimensional spectral search, the authors proposed the reduced-dimension multiple signals classification (MUSIC) algorithm with the SD (SD-RD-MUSIC) co-array of the nested array by discarding the NC phase. Resultantly, the proposed SD-RD-MUSIC co-array algorithm can resolve more signals and obtain more accurate direction of arrival estimates. In addition, the effectiveness and superiority of the proposed method are validated by numerical simulations.

关键词： array signal processing direction-of-arrival estimation signal classification nested array sum-difference co-array sum co-array typical difference co-array co-array algorithm noncircular signals direction of arrival estimation DOA estimation algorithm enhanced degrees of freedom co-array aperture enlarged array aperture two-dimensional spectral search reduced-dimension multiple signals classification MUSIC algorithm SD-RD-MUSIC co-array algorithm

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A new nested array for direction-of-arrival estimation

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MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2020年第2期31卷 663-672页

作者： Li, Zhenghan Huang, Huiping Liao, Bin Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Tech Univ Darmstadt Signal Proc Grp Darmstadt Germany

In this paper, the problem of direction-of-arrival (DOA) estimation with a new nested array is addressed. It is known that the original two-level nested array is composed of two uniform linear arrays (ULAs), and there exist some redundant elements in their difference co-array. In order to reduce the redundancy, a new array configuration which consists of a ULA and a non-uniform linear array, rather than two ULAs, is proposed. On one hand, the proposed array can effectively increase degrees of freedom as well as virtual array aperture. On the other hand, it has a group of continuous lags in the virtual array. Thanks to the advantages of the proposed array configuration, higher resolution and better estimation performance of DOA estimation can be achieved. Simulation results show the superiority of the proposed array structure.

关键词： Direction-of-arrival estimation nested array Degrees of freedom Virtual array aperture

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Deep Alternating Projection Networks for Gridless DOA Estimation With nested array

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IEEE SIGNAL PROCESSING LETTERS 2022年 29卷 1589-1593页

作者： Su, Xiaolong Hu, Panhe Liu, Zhen Shi, Junpeng Li, Xiang Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China

Recently, deep unfolding networks with interpretable parameters have been widely utilized in direction of arrival (DOA) estimation due to the faster convergence speed and better generalization ability. However, few consider the nested array for gridless DOA estimation. In this letter, we propose a deep alternating projection network to address the problem. We first convert the covariance matrix into a measurement vector in the form of atomic norm, which can reduce the matrix dimension during projection. We then train the proposed network to alternately obtain the positive semi-definite matrix and the corresponding irregular Hermitian Toeplitz matrix, where the loss function is derived by employing the trace of network output. Finally, we apply the irregular root Multiple Signal Classification (MUSIC) method to obtain gridless DOA via nested array. We demonstrate that the proposed networks can accelerate the convergence rate and reduce computational cost. Simulations verify the performance of proposed networks in comparison with the existing methods.

关键词： Direction-of-arrival estimation Estimation Covariance matrices Atomic measurements Eigenvalues and eigenfunctions Convergence Computational efficiency Gridless direction of arrival (DOA) deep unfolding network alternating projection nested array

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A Novel nested array for Real-Valued Sources Exploiting array Motion

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IEEE SIGNAL PROCESSING LETTERS 2021年 28卷 1375-1379页

作者： Patra, Rajen Kumar Dhar, Anindya Sundar Def Res & Dev Org DRDO Radar Syst Grp Integrated Test Range ITR Balasore 756025 India IIT Kharagpur Elect & Elect Commun Dept Kharagpur 721302 W Bengal India

In this letter, a novel nested array is proposed which not only increases the degrees of freedom (DOF) but also reduces the mutual coupling that occurs in the general nested array structure by moving the original array only half wavelength. Here we deal with real-valued sources like AM or BPSK whereby we can explore the property of the sum coarray along with the difference coarray of the synthetic array (SA). It is assumed that the sensing environment does not change over the array motion of half wavelength. First, we develop the novel nested array and prove that the sum difference coarray (SDCA) of SA is hole-free up to a certain number. Then we provide closed-form expressions for the number of unique and consecutive lags of the SDCA of SA. We also optimize the array for a given number of sensors. Simulation results show the superiority of the proposed array over other existing arrays.

关键词： Sensor arrays Sensors array signal processing Manifolds Multiple signal classification Simulation Narrowband array motion DOA estimation nested array sum difference coarray

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Off-grid DOA estimation for nested array using atomic norm minimisation

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ELECTRONICS LETTERS 2018年第23期54卷 1344-1345页

作者： Jiang, Hong Tang, Wen-Gen Pang, Shuai-Xuan Jilin Univ Coll Commun Engn Changchun 130012 Jilin Peoples R China

nested array (NA) has received much attention for providing more degrees of freedom compared with the traditional uniform linear array. An off-grid compressed sensing (CS) method is proposed using atomic norm minimisation for NA to identify the unknown direction-of-arrival of multiple sources, which overcomes the drawback of grid-mismatch effect of the conventional CS algorithms such as orthogonal matching pursuit, and achieves better performance than the subspace methods. Simulation results verify the effectiveness of the proposed method.

关键词： direction-of-arrival estimation compressed sensing minimisation nested array atomic norm minimisation degrees of freedom traditional uniform linear array off-grid compressed sensing method direction-of-arrival grid-mismatch effect conventional CS algorithms off-grid DOA estimation orthogonal matching pursuit subspace methods

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2-D Direction FindingWith Pair-Matching Operation for L-Shaped nested array

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IEEE COMMUNICATIONS LETTERS 2021年第3期25卷 975-979页

作者： Zheng, Zhi Mu, Shilin Univ Elect Sci & Technol China UESTC Sch Informat & Commun Engn Chengdu 611731 Peoples R China UESTC Inst Elect & Informat Engn Guangdong Dongguan 523808 Peoples R China

In this letter, we devise a new approach for two-dimensional (2-D) direction finding using an L-shaped array structured by two nested arrays. By vectorizing the covariance matrices of the nested array outputs, the elevation and azimuth angles of the sources are firstly estimated separately. The pair-matching of the elevation and azimuth angle estimates is then achieved employing a permutation matrix which is obtained by solving an optimization problem with the array manifold matrices, cross-covariance matrix, and the source covariance matrix. Unlike the existing schemes, the proposed approach reconstructs the source covariance matrix in the coarray domain. Therefore, it can deal with more sources than the number of sensors in each nested array. Numerical results demonstrate the advantage of the proposed approach over the existing algorithms.

关键词： Covariance matrices Azimuth Signal to noise ratio Manifolds Sensor arrays array signal processing Direction finding nested array L-shaped nested array pair-matching difference coarray

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Hybrid precoding for heterogeneous cloud radio access network based on nested array

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AD HOC NETWORKS 2017年 58卷 118-126页

作者： Chen, Na Sun, Songlin Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Key Lab Trustworthy Distributed Comp & Serv Minist Educ Beijing Peoples R China

Future wireless system meets the requirements of high network capacity but is suffering from the problems of high interference and resource shortage. In this paper, we develop a hybrid precoding solution by applying the heterogeneous cloud radio access network (H-CRAN) with remote radio heads (RRHs) equipped with nested arrays for a better system control and higher efficiency. We first summarize the related work and highlight our contribution. Then we propose the hybrid precoding scheme after introducing the system model based on the H-CRAN architecture and the nested array. For the proposed hybrid precoding scheme, the RRHs first identify the interference sources around them, then report it to the baseband unit (BBU) pool. The BBU pool identifies the sources of interference and searches for available resource for the RRH. As the frequency may be reused in specific area, the eNB processes the hybrid precoding considering the null-space of the victim users so that the interference can be avoided. Finally, we evaluate the scheme and prove its performance with numerical results. (C) 2016 Elsevier B.V. All rights reserved.

关键词： H-CRAN Massive MIMO nested array Null-space Hybrid precoding

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Augmented Multi-Subarray Dilated nested array With Enhanced Degrees of Freedom and Reduced Mutual Coupling

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 1387-1399页

作者： Chen, Hua Lin, Hongguang Liu, Wei Wang, Qing Shen, Qing Wang, Gang Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Zhejiang Key Lab Mobile Network Applicat Technol Ningbo 315211 Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England Tianjin Univ Sch Elect Informat Engn Tianjin 300072 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

Sparse linear arrays (SLAs) can be designed in a systematic way, with the ability for underdetermined DOA estimation where a greater number of sources can be detected than that of sensors. In this paper, as the first stage, a new systematic design named multi-subarray dilated nested array (MDNA), whose difference co-array (DCA) can be proved to be hole-free, is firstly proposed by introducing a sparse ULA and multiple identical dense ULAs with appropriate sub-ULA spacings. The MDNA will degenerate into the nested array under specific conditions, and the uniform degrees of freedom (uDOFs) of MDNA is larger than that of its parent nested array. On the basis of MDNA, to reduce the mutual coupling effect, an augmented multi-subarray dilated nested array (AMDNA) is constructed by migrating some elements of the dense segments of MDNA, without reducing the number of uDOFs. Several theoretical properties of the proposed array structures are proved, and simulation results are provided to demonstrate the effectiveness and superiority of the proposed AMDNA over some existing sparse arrays.

关键词： Sensor arrays Sensors Vectors Estimation Direction-of-arrival estimation array signal processing Mutual coupling Sparse array DOA estimation nested array multi-subarray dilated nested array augmented multi-subarray dilated nested array

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Direction estimation using a two-level nested array with gain-phase error calibration

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SIGNAL IMAGE AND VIDEO PROCESSING 2022年第3期16卷 745-751页

作者： Prabha, G. Natarajamani, S. Kirthiga, S. Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Elect & Commun Engn Coimbatore Tamil Nadu India

The direction of arrival of multiple signals arriving on a non-uniform linear nested array in the presence of array errors due to gain and phase perturbations is considered in this paper. An l-2 norm minimization is proposed for gain and phase error calibration and the direction of signals is estimated using the spectral search method. The proposed method iteratively minimizes the error and reconstructs the covariance matrix subsequently reducing the effect of the error. The results obtained from simulations illustrate the capability of the proposed method in overcoming the performance deterioration due to errors in a nested array.

关键词： nested array Direction of arrival Gain-phase error Compensation

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The Phase Fractional Lower-Order Moment Based DPD Algorithm of Non-circular Sources for nested array Under Impulsive Noise

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WIRELESS PERSONAL COMMUNICATIONS 2024年第1期139卷 183-208页

作者： Li, Jiaqi Zhang, Xiaofei Jiang, Kang Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 211106 Peoples R China

This study focuses on the direct position determination (DPD) of non-circular (NC) sources with multiple nested arrays (NAs). Under impulsive noise modeled as a complex symmetric alpha stable (S alpha S\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\text{S}}{\upalpha }{\text{S}} $$\end{document}) process, traditional DPD algorithms suffer from significant degradation in localization performance. To address this problem, a phase fractional lower-order moment (PFLOM) based DPD algorithm for NC sources using multiple NAs, combined with a reduced-dimension subspace data fusion (RD-SDF) technique, termed PNNR-SDF, is proposed in this paper. By employing the PFLOM-based covariance matrix instead of the second-order covariance matrix, the sharp amplitude of the impulsive noise can be effectively suppressed, thus improving the estimation accuracy. Moreover, the RD-SDF technique is exploited to reduce high computational complexity. Numerical results demonstrate the superiority of the PNNR-SDF method compared to conventional DPD algorithms under impulsive noise.

关键词： Direct position determination (DPD) Non-circular (NC) signals nested array Phase fractional lower-order moment (PFLOM) Reduced-dimension subspace data fusion (RD-SDF)

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