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2D DOA Estimation Algorithm by nested Acoustic Vector-Sensor array

CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2022年第2期41卷 1115-1130页

作者： Liu, Sheng Zhao, Jing Zhang, Yu Wu, Decheng Tongren Univ Sch Data Sci Tongren 554300 Peoples R China Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China Chongqing Univ Posts & Telecommun Sch Automat Chongqing 400065 Peoples R China

In this paper, a two-dimensional direction-of-arrival (DOA) estimation algorithm based on nested acoustic vector-sensor (AVS) array is introduced. The minimum unit interval of the used nested AVS array is integral multiple of half-wavelength of signal. Firstly, four selection matrices are used to recombine four received vectors, by which four special cross-covariance matrices are obtained to form an extended block covariance matrix. Then, a set of parameters on elevation angles are estimated by traditional estimation of signal parameter via rotational invariance technique (ESPRIT) algorithm. Using these parameters, unambiguous elevation angles can be obtained without spectral peak search and added eigenvalue decomposition. At last, using the estimated elevation angles, the azimuth angles can be estimated by array manifold matching algorithm. The proposed algorithm can distinguish more signals than some existing ESPRIT algorithms. Many simulation results can prove the performance of the proposed algorithm in improving the accuracy of DOA estimation. Simulation results also show that the unit interval of optimal nested AVS array should be larger than half-wavelength of received signal.

关键词： nested array AVS DOA estimation ESPRIT algorithm

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Augmented Tensor MUSIC for DOA Estimation Using nested Acoustic Vector-Sensor array

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IEEE SIGNAL PROCESSING LETTERS 2022年 29卷 1624-1628页

作者： Qu, Xinghao Lou, Yi Zhao, Yunjiang Lu, Yinheng Qiao, Gang Harbin Engn Univ Acoust Sci & Technol Lab Coll Underwater Acoust Engn Harbin 150001 Peoples R China Harbin Engn Univ Minist Ind & Informat Technol Key Lab Marine Informat Acquisit & Secur Harbin 150001 Peoples R China Yichang Testing Tech Res Inst Yichang 443003 Peoples R China

nested acoustic vector sensor (AVS) arrays have attracted growing interest, and their performance can be further improved by assembling AVSs with spatially separated (SS) configurations. Following this trend, we propose an augmented tensor-MUSIC (AT-MUSIC) method for SS-AVSs while exploiting the inherent multidimensional structure of the array data. The implications of "augmented" are twofold: (i) conventional tensor models based on co-centered AVSs are generalized to the case of SS-AVSs, and (ii) available information is fully utilized by judiciously arranging and aggregating the received data. Finally, we show the enhanced performance of AT-MUSIC in simulations.

关键词： Tensors Sensor arrays Estimation Correlation Direction-of-arrival estimation Apertures Acoustic arrays Direction-of-arrival estimation acoustic vector sensor nested array tensor modeling

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Low-Cost nested-MIMO array for Large-Scale Wireless Sensor Applications

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SENSORS 2017年第5期17卷 1105-1105页

作者： Zhang, Duo Wu, Wen Fang, Dagang Wang, Wenqin Cui, Can Nanjing Univ Sci & Technol Sch Elect Engn & Optoelect Technol Ministerial Key Lab JGMT Nanjing 210094 Jiangsu Peoples R China Univ Elect Sci & Technol China Sch Commun & Informat Engn Chengdu 611731 Peoples R China

In modern communication and radar applications, large-scale sensor arrays have increasingly been used to improve the performance of a system. However, the hardware cost and circuit power consumption scale linearly with the number of sensors, which makes the whole system expensive and power-hungry. This paper presents a low-cost nested multiple-input multiple-output (MIMO) array, which is capable of providing O (2N(2)) degrees of freedom (DOF) with O (N) physical sensors. The sensor locations of the proposed array have closed-form expressions. Thus, the aperture size and number of DOF can be predicted as a function of the total number of sensors. Additionally, with the help of time-sequence-phase-weighting (TSPW) technology, only one receiver channel is required for sampling the signals received by all of the sensors, which is conducive to reducing the hardware cost and power consumption. Numerical simulation results demonstrate the effectiveness and superiority of the proposed array.

关键词： nested array single receiver channel time-sequence-phase-weighting (TSPW) multiple-input multiple-output (MIMO) degrees of freedom (DOF) large-scale array

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2-D direction finding using parallel nested arrays with full co-array aperture extension

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SIGNAL PROCESSING 2021年 178卷 107795-107795页

作者： He, Jin Li, Linna Shu, Ting Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Key Lab Intelligent Sensing & Recognit Shanghai 200240 Peoples R China Shanghai Aerosp Elect Commun Equipment Res Inst Shanghai 201109 Peoples R China

Sparse arrays, such as nested arrays, are able to resolve more sources than sensors because their difference co-arrays can provide O(L-2) degrees-of-freedom (DOFs) with L physical sensors. Most of the existing nested array direction-finding algorithms apply the spatial smoothing technique to realize this DOFs enhancement. One shortcoming of this type of methods is the efficient co-array aperture is reduced in processing the spatial smoothing, resulting in the DOFs are not fully utilized. In this paper, we propose a new approach using two parallel linear nested arrays to contribute full DOFs for two-dimensional direction-finding. To exploit the entire DOFs, we perform the vectorization of multiple fourth-order cumulant matrices and take the average of their co-array covariance matrices, instead of the spatial smoothing of the vectorization of the data covariance matrix. Based on a well-posed identification analysis, we show that the proposed approach can identify the number of sources approximately three times than the algorithms using the spatial smoothing technique. For example, for a two-level parallel nested array of 2 + 2 sensors in each subarray, the maximum number of sources that can be resolved by the proposed approach is 32, whereas, for most of the existing algorithms, the number reduces to 10. (C) 2020 Elsevier B.V. All rights reserved.

关键词： array signal processing direction finding nested array difference co-array

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Robust adaptive beamforming in nested array

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SIGNAL PROCESSING 2015年 114卷 143-149页

作者： Yang, Jie Liao, Guisheng Li, Jun Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China

In this communication, we consider the problem of designing robust adaptive beamformer in the newly proposed nested array, where we maintain distortionless response towards the true desired signal and suppress more interferences than the number of actual physical sensors. The essence of our proposed method is to reconstruct the interference-plus-noise covariance matrix and estimate the true desired signal steering vector. The reconstruction process is performed by projecting the spatially smoothed matrix into interference subspace. The estimation process is performed by solving an optimization problem based on minimizing the beamformer sensitivity and enforcing the estimate not to converge to any of the interference steering vectors. We also show that the constructed optimization problem can be efficiently computed at a comparable cost with that of the standard Capon beamformer (SCB). The effectiveness of the proposed method is verified through numerical simulations. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Robust adaptive beamforming nested array Interference-plus-noise covariance matrix reconstruction Steering vector estimation

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The DOA Estimation Algorithm Based on Virtual Domain Block Matching for nested arrays in the Presence of Mutual Coupling

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IEEE ACCESS 2025年 13卷 20427-20436页

作者： He, Shun Sun, Nan Yang, Zhiwei Qin, Le Univ Sci & Technol China Sch Commun & Informat Engn Hefei 710600 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710126 Peoples R China

Mutual coupling severely impact Direction of Arrival (DOA) estimation, and the existence of mutual coupling can lead to significant errors in the received signal model of the array. Using algorithms to reduce it is an effective approach. Few algorithms are suitable for nested arrays, and due to incomplete elimination or the loss of some data, they performance deteriorates in the small number of snapshot and low signal-to-noise ratio environments. To address these issues, this paper proposes a Virtual Domain Block Matching DOA estimation algorithm for nested arrays in mutual coupling. The algorithm begins by partitioning the mutual coupling matrix of nested arrays, then applies a classical separation method used in Uniform Linear array to obtain the separation parameters. Next, it reconstructs an equivalent single-snapshot received data vector in the virtual domain. A sparse reconstruction optimization model is then formulated by exploiting signal sparsity and virtual domain block matching. Solving this model provides a block representation of DOA estimation, where the $l_nested array$ norm of each block yields the DOA estimation for the actual incident signals. Extensive simulation results demonstrate that this algorithm fully utilizes the high degrees of freedom of the nested arrays to achieve underestimation under unknown mutual coupling and maintains high accuracy in small snapshot and low SNR. Additionally, the algorithm is not sensitive to the grid's size. The experimental results show that this algorithm outperforms several existing methods.

关键词： Mutual coupling Sensor arrays Direction-of-arrival estimation Estimation Vectors Sparse matrices Mathematical models Covariance matrices Matrix converters Optimization direction of arrival virtual domain block matching nested array degrees of freedom

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Super Transformed nested arrays for DOA Estimation of Non-Circular Signals with Reduced Mutual Coupling 24

Super Transformed Nested Arrays for DOA Estimation of Non-Ci...

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3rd International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)

作者： Guo, Haodong Li, Jiajie Yi, Zelong Fang, Jiaxiong Chen, Hua Wang, Gang Ningbo Univ Ningbo Peoples R China

ISBN: (纸本)9798400709777

Recently, numerous sparse linear array structures have been developed to improve accuracy in Direction of Arrival (DOA) estimation for non-circular signals. The primary objective is to expand the continuous segment of the sum-difference coarray, thus increasing the uniform Degrees of Freedom (uDOFs). To balance a high number of uDOFs and low mutual coupling, a class of Super Transformed nested arrays (STNAs) is introduced based on the existing Transformed nested arrays (TNAs). By migrating certain sensors of original TNA, the mutual coupling issue caused by the dense subarray is alleviated. Following this scheme, two array structures are presented: 2-STNA and 3-STNA. Numerical results are provided to show the excellence of their performance in the presence of strong mutual coupling.

关键词： Direction of Arrival estimation nested array sum-difference coarray sparse array mutual coupling

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nested array Processing for Distributed Sources

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IEEE SIGNAL PROCESSING LETTERS 2014年第9期21卷 1111-1114页

作者： Han, Keyong Nehorai, Arye Washington Univ Preston M Green Dept Elect & Syst Engn St Louis MO 63130 USA

We consider the problem of using linear nested arrays to estimate the directions of arrival (DOAs) of distributed sources and to detect the source number, where we have more sources than actual physical sensors. Angular spread, caused by the multipath nature of the distributed sources, makes the commonly used point-source assumption challenging. We establish the signal model for distributed sources, using a nested array. Due to the characteristics of distributed sources, the regular spatial smoothing technique, which is used to exploit the increased degrees of freedom provided by the co-array, no longer works. We thus propose a novel spatial smoothing approach to circumvent this problem. Based on the analytical results, we construct the corresponding DOA estimation and source number detection methods. The effectiveness of the proposed methods is verified through numerical examples.

关键词： Direction of arrival estimation distributed source nested array source number detection

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Noncircular Distributed Source DOA Estimation with nested arrays via Reduced-Dimension MUSIC

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SENSORS 2024年第20期24卷 6653-6653页

作者： Chen, Kaiyuan Chen, Weiyang Li, Jiaqi Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 211106 Peoples R China

This paper focuses on the direction-of-arrival (DOA) estimation for noncircular coherently distributed (CD) sources with nested arrays. Usually, for point sources, sparse arrays have the potential to improve the estimation performance of algorithms by obtaining more degrees of freedom. However, algorithms have to be reconsidered for CD sources with sparse arrays and many problems arise. One thorny problem is the disappearance of displacement invariance of the virtual array manifold constructed by the virtualization technique. To deal with this issue, a nested array processing method for CD sources transmitting noncircular signals is proposed in this paper. Firstly, we construct the virtual sum-and-difference co-array by leveraging the noncircular quality of signals with a nested array. Then, an approximation is made to degrade CD sources into point sources. In this way, spatial smoothing techniques can be applied to restore the rank. Finally, in order to reduce the complexity, we modify the reduced-dimension MUSIC to estimate DOAs through a one-dimensional peak-searching procedure. The simulation results prove the superiority of our algorithm against other competitors.

关键词： DOA estimation coherently distributed source nested array reduced-dimension MUSIC

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HALF INVERTED nested arrayS WITH LARGE HOLE-FREE FOURTH-ORDER DIFFERENCE CO-arrayS 47

HALF INVERTED NESTED ARRAYS WITH LARGE HOLE-FREE FOURTH-ORDE...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Chen, Yuan-Pon Liu, Chun-Lin Natl Taiwan Univ Dept Elect Engn Taipei 10617 Taiwan Natl Taiwan Univ Grad Inst Commun Engn Taipei 10617 Taiwan

ISBN: (纸本)9781665405409

Sparse arrays with fourth-order cumulant processing can identify up to O(N-4) source directions with only N physical sensors. To achieve this property, the fourth-order difference co-array is preferable to own a contiguous segment with no holes. However, existing sparse array designs either have holes in the co-array of size O(N-4) or own fewer than O(N-4) elements in the hole-free co-array. This paper proposes the half inverted nested array (HINA), which consists of a nested array and an inverted, scaled, and shifted nested array. By maximizing the size of the co-array, it can be shown that HINA with the optimal parameters possesses a hole-free fourth-order difference co-array of size O(N-4). Numerical examples demonstrate the improved DOA estimation performance of HINA.

关键词： Sparse arrays fourth-order difference co-arrays nested array direction of arrival estimation

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