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A New nested array Configuration With Increased Degrees of Freedom

IEEE ACCESS 2018年 6卷 1490-1497页

作者： Huang, Huiping Liao, Bin Wang, Xiaoye Guo, Xiansheng Huang, Jianjun Shenzhen Univ Coll Informat Engn Shenzhen 518060 Peoples R China Univ Elect Sci & Technol China Dept Elect Engn Chengdu 610051 Sichuan Peoples R China

In this paper, a new linear array configuration based on the concept of two-level nested array is proposed. Specifically, the proposed array configuration consists of two uniform linear arrays (ULAs) plus a separate sensor with appropriate spacing apart. Compared with the original two-level nested array, the degrees of freedom of our proposed array configuration can be increased by 2L - 6 for an array with L physical sensors. Moreover, the virtual array aperture can be enlarged by nearly (L-2/2 - L - 2)(d(1), where d(1) is the inter-element spacing of the first ULA. Owing to these advantages, our proposed array configuration has enhanced resolution and achieves better performance in parameter estimation, such as direction-of-arrival (DOA) estimation. Numerical simulations of DOA estimation exhibit the effectiveness and superiority of this array configuration.

关键词： nested array array configuration degrees of freedom virtual array aperture parameter estimation direction-of-arrival

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Channel estimation for massive MIMO with 2-D nested array deployment

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PHYSICAL COMMUNICATION 2017年第Part2期25卷 432-437页

作者： Zhu, Fangqi Wu, Na Liang, Qilian Univ Texas Arlington Dept Elect Engn Arlington TX 76010 USA

The problem of channel estimation in 5G is regarded as the one of the bottleneck problems due to its complexity related with large number of antenna elements at the BS side and more narrower beams when choosing high frequency such as millimeter wave. In this paper, we study the channel estimation problem for massive MIMO with a new antenna array at the base station (BS) side. The randomly deployed single antenna user equipments (UEs) within a single cell in the cellular network comprise of a random array. Based on the geometric channel model, using multiple snapshots of beamforming and combining vectors at the BS and UEs side respectively, the problem is formulated as a sparsity-aware problem and the coordinate descent algorithm is employed to retrieve the significant channel gain. Simulation results show the effective of the algorithm under two different scenarios with high SNR and low SNR respectively and for both cases, we can find the significant paths with properly chosen penalization parameter.. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Massive MIMO Channel estimation nested array Sparsity-aware

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2-D DOA Estimation Based on Sparse Bayesian Learning for L-Shaped nested array

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IEICE TRANSACTIONS ON COMMUNICATIONS 2019年第5期E102B卷 992-999页

作者： Chen, Lu Bi, Daping Pan, Jifei Natl Univ Def Technol Hefei Anhui Peoples R China

In sparsity-based optimization problems for two dimensional (2-D) direction-of-arrival (DOA) estimation using L-shaped nested arrays, one of the major issues is computational complexity. A 2-D DOA estimation algorithm is proposed based on reconsitution sparse Bayesian learning (RSBL) and cross covariance matrix decomposition. A single measurement vector (SMV) model is obtained by the difference coarray corresponding to one-dimensional nested array. Through spatial smoothing, the signal measurement vector is transformed into a multiple measurement vector (MMV) matrix. The signal matrix is separated by singular values decomposition (SVD) of the matrix. Using this method, the dimensionality of the sensing matrix and data size can be reduced. The sparse Bayesian learning algorithm is used to estimate one-dimensional angles. By using the one-dimensional angle estimations, the steering vector matrix is reconstructed. The cross covariance matrix of two dimensions is decomposed and transformed. Then the closed expression of the steering vector matrix of another dimension is derived, and the angles are estimated. Automatic pairing can be achieved in two dimensions. Through the proposed algorithm, the 2-D search problem is transformed into a one-dimensional search problem and a matrix transformation problem. Simulations show that the proposed algorithm has better angle estimation accuracy than the traditional two-dimensional direction finding algorithm at low signal-to-noise ratio and few samples.

关键词： two-dimensional direction finding nested array sparse Bayesian learning matrix decomposition complexity reduction

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Direction-of-Arrival Estimation in a Mixture of Multiple Circular and Non-Circular Signals Using nested array

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IEEE ACCESS 2020年 8卷 105237-105249页

作者： Han, Peng Xu, Haiyun Ba, Bin Zhang, Yankui Natl Digital Switching Syst Engn & Technol Res Ct Zhengzhou 450001 Peoples R China

In practice, there are many circular and non-circular signals due to multipath propagation and various modulations. Conventional direction-of-arrival (DOA) estimation in a mixture of circular and non-circular signals cannot distinguish two kind of signals and detect more sources than number of sensors at the same time. This paper proposes a novel separation algorithm based on elliptic covariance matrix (ECM) which possesses accurate DOA estimation and high degrees of freedom (DOF) with low complexity. Firstly it estimates non-circular signals using ECM which contains non-circular information merely. Considering that the virtual array generated from nested array using ECM is inconsecutive, a matrix completion method via nuclear norm minimization is also included and as a result, the freedom degrees are further extended. On the basis of ECM, the paper also introduces a separation algorithm through subtraction of two reconstructed Toeplitz covariance matrix (CM). Detailed analysis and theoretically proof is presented subsequently and DOAs of circular signals can be obtained after separation. Simulation results show that the proposed algorithm can realize underdetermined estimation and get accurate DOAs while two kind of signals are separated simultaneously.

关键词： Direction-of-arrival nested array non-circular signals separation technique matrix completion

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nested array for Antenna Deployment in Massive MIMO with Spectrum Efficiency 5

Nested Array for Antenna Deployment in Massive MIMO with Spe...

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5th IEEE International Conference on Mobile Services (MS)

作者： Liang, Qilian Univ Texas Arlington Dept Elect Engn Arlington TX 76019 USA

ISBN: (纸本)9781509026258

The nested array was firstly introduced to perform array processing with increased degrees of freedom using much fewer physical sensors In this paper, we apply nested array to antenna deployment in massive MIMO, which can tremendously reduce the number of antennas, and achieve the same performance. Besides, this deployment for massive MIMO has also spectrum efficiency. As a case study, we demonstrate that real-world LTE/LTE-A mobile service data traffic is quite fluctuating, and adaptive bandwidth allocation should be performed to increase spectrum efficiency.

关键词： Massive MIMO LTE/LTE-A Mobile Service Data nested array

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Estimation of directions of arrival of multiple distributed sources for nested array

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SIGNAL PROCESSING 2017年第0期130卷 315-322页

作者： Wen, Chao Shi, Guangming Xie, Xuemei Xidian Univ Collaborat Innovat Ctr Informat Sensing & Underst State Key Lab Integrated Serv Networks Sch Elect Engn Xian 710071 Shaanxi Peoples R China

We consider the problem of estimating the nominal directions of arrival (DOA) of multiple distributed sources using nested array. In practical applications, angular spread of distributed source arises due to the multipath propagation and local scattering around the vicinity of a source. DOA estimation methods based on point-source assumption are known to severely degrade in the presence of the angular spread. To solve this problem, we develop a distributed source model based approach for a nested array. Based on the concept of difference co-array, redundancy averaging technique is applied to the sample covariance matrix. By exploiting the annihilating property of the redundancy averaged terms, an efficient method based on annihilating filter (AF) is proposed. This method can be applied to the case of more sources compared with the actual physical sensors without a priori knowledge of the spreading parameters. Whereas the sample covariance matrix is corrupted by either noise or estimation error due to finite snapshots, so an AF method with structured low rank approximation (SLRA) is further proposed, which results in a better estimate. Finally, numerical simulations demonstrate the effectiveness of the proposed methods. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Angular spread Direction of arrival Distributed sources nested array Structured low rank approximation

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Direct Localization of Multiple Noncircular Sources With a Moving nested array

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IEEE ACCESS 2019年 7卷 101106-101116页

作者： Kumar, Gowri Ponnusamy, Palanisamy Amiri, Iraj Sadegh Natl Inst Technol Dept Elect & Commun Engn Tiruchirappalli 620015 Tamil Nadu India Ton Duc Thang Univ Adv Inst Mat Sci Computat Opt Res Grp Ho Chi Minh City 758307 Vietnam Ton Duc Thang Univ Fac Appl Sci Ho Chi Minh City Vietnam

This paper proposes a subspace data fusion (SDF)-based direct positioning determination (DPD) method for noncircular sources with a moving nested array (NA). The DPD algorithms using a uniform linear array (ULA) and coprime (CP) array are available in the existing literature. Sparse arrays have larger apertures than ULAs, which enhances degrees of freedom (DOFs). However, the existing sparse CP arrays have limited DOF and also have poor detecting ability. In this paper, the physical sensors of NA are rearranged to obtain a difference sum NA (DSNA) and second-order super NA (II-SNA). The rearranged NA sensors are seen to increase array aperture. The noncircular characteristics of the sources are also used to improve accuracy in positioning. To obtain high DOF in NA, DSNA, and II-SNA, the covariance matrices are vectorized. The coherency of the virtual array is resolved by applying the spatial smoothing technique. Finally, the SDF-based DPD is used to establish the cost function and the target is localized. The simulation results are provided for the NA, DSNA, and II-SNA and are compared with the existing CP array DPD algorithm. The results show that the proposed method shows significant enhancement in localization accuracy. The Cramer-Rao lower bound (CRLB), complexity, and performance comparisons are also described in this paper.

关键词： Difference and sum nested array direct position determination nested array second order super nested array

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Underdetermined DOA Estimation Algorithm Based on an Improved nested array

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WIRELESS PERSONAL COMMUNICATIONS 2020年第4期112卷 2423-2437页

作者： Wang, Lanmei Hui, Zhe Wang, Shuzhen Wang, Guibao Xidian Univ Sch Phys & Optoelect Engn Xian 710071 Peoples R China

A nonuniform array group is proposed to solve the inflexibility problem of existing nested arrays. The array structure in the group is not required to be composed of two or more uniform linear sub-arrays on the premise of maintaining the same degree of freedom as that of nested arrays. Compared with nested arrays, a nonuniform array group is easier to arrange in airborne radar or limited physical space. For the nonuniform array group proposed in this study, an array is randomly selected for deployment, and the covariance matrix of the data received by the array is quantised. Accordingly, an algorithm for data de-redundancy and sorting is proposed, and the virtual array receiving data are obtained. Lastly, the target direction of arrival (DOA) is estimated using a sparse reconstruction method without losing array aperture. Simulation results show that the proposed method can achieve accurate DOA estimations at low signal-to-noise ratio and snapshot number.

关键词： nested array Nonuniform array group Degrees of freedom Virtual array Sparse reconstruction

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Real-Valued Deep Unfolded Networks for Off-Grid DOA Estimation via nested array

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第4期59卷 4049-4062页

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

Recently, deep unfolded networks have been widely utilized in direction of arrival (DOA) estimation due to the reduced computational complexity and improved estimation accuracy. However, few consider the nested array for off-grid DOA estimation, where the estimated DOAs are not on the prespecified grids. In this article, we propose a deep unfolded FOCal underdetermined system solver network and a deep unfolded alternating direction method of multiplies to address the problem, which respectively aim to improve estimation accuracy and further reduce computational complexity. We first apply first-order Taylor expansion and vectorize the covariance matrix into a real-valued single snapshot for network input. We then train the proposed networks to obtain on-grid DOA spatial spectrum and off-grid values, where the off-grid DOA estimation is calculated by the peaks of off-grid DOA spatial spectrum and corresponding off-grid values. We demonstrate that the proposed networks with interpretable parameters can accelerate the convergence rate and achieve better generalization. Simulations verify the performance of proposed networks in comparison with the existing methods.

关键词： Direction-of-arrival estimation Estimation Covariance matrices Manifolds Dictionaries Convergence Convex functions Alternating direction method of multiplies (ADMM) direction of arrival (DOA) real-valued deep unfolded network FOCal underdetermined system solver (FOCUSS) nested array

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Off-Grid DOA Estimation for Noncircular Signals via Block Sparse Representation Using Extended Transformed nested array

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IEEE SIGNAL PROCESSING LETTERS 2023年 30卷 130-134页

作者： Yuan, Jiawen Zhang, Gong Leung, Henry Ma, Shaodan Nanjing Univ Aeronaut & Astronaut Key Lab Radar Imaging & Microwave Photon Nanjing 211100 Peoples R China Univ Calgary Dept Elect & Software Engn Calgary AB T2N 1N4 Canada Univ Macau State Key Lab Internet Things Smart City Macau 999078 Peoples R China

An off-grid direction-of-arrival (DOA) estimation method based on block sparse representation is proposed to localize the strictly noncircular (NC) sources utilizing an extended transformed nested array (ETNA). This novel off-grid DOA estimation algorithm effectively promotes spatial distribution information mining. Furthermore, it is conducive to providing stable signal recovery, which refines the DOA estimation precision with interpolation over a coarse grid. We then combine the above algorithm with the designed ETNA to improve the detection performance. The ETNA is an optimal displacement on the existing TNA, which enlarges the degree of freedom (DOF) and lengthens the maximum contiguous segment from the derived virtual array. Simulation results demonstrate its superiority in estimation performance and DOF.

关键词： Off-grid DOA estimation block sparse representation NC nested array DOF

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