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8th IEEE Sensor array and Multichannel Signal Processing Workshop (SAM)

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

ISBN: (纸本)9781479914814

We consider the problem of using linear nested arrays to estimate the direction of arrival (DOA) of spatially distributed sources and detect the source number, where we have more sources than the actual physical sensors. Angular spread, caused by the multipath nature of distributed sources, makes the commonly used point source assumption ineffective. In this paper, 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 corresponding DOA estimation and source number detection methods. The effectiveness of the proposed methods is verified through numerical examples.

关键词： Distributed source nested array direction of arrival estimation source number detection

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A DOA Estimation Method of coherent and uncorrelated sources based on nested arrays

A DOA Estimation Method of coherent and uncorrelated sources...

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Annual Summit and Conference of the Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA ASC)

作者： Xie, Julan Cheng, Fanghao He, Zishu Li, Huiyong Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

ISBN: (纸本)9781728132488

This paper presents a novel way to estimate the DOA of coherent and uncorrelated sources for nested array. At first, by using the vectorization of the covariance matrix of the received data, the LASSO algorithm with a modified dictionary matrix based on compressed sensing is applied to estimate a vector containing all signal powers. Then, the estimated vector is reconstructed into the covariance matrix of coherent and uncorrelated sources. A peak searching to estimate DOAs can be performed by using the diagonal elements of this reconstructed matrix. In this presented method, the advantage of fully utilizing the degree of freedom of nested arrays is preserved. Theoretical analysis and simulation results show the effectiveness of the proposed algorithm.

关键词： DOA estimation uncorrelated signals coherent signals nested array compressive sensing

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A DOA Estimation Method in the presence of unknown mutual coupling based on nested arrays

A DOA Estimation Method in the presence of unknown mutual co...

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Annual Summit and Conference of the Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA ASC)

作者： Xie, Julan Cheng, Fanghao He, Zishu Li, Huiyong Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

ISBN: (纸本)9781728132488

A novel DOA method is proposed to deal with the DOA estimation in the presence of the unknown mutual coupling for nested arrays. By using a new expression of the steering matrix in the presence of mutual coupling, a novel expression of the receiving data vector in the virtual array field is available. Then, based on a modified direction matrix constructed with block matrix, which relates to space discretized sampling grid, the sparse Bayesian compressive sensing method applies to estimate a vector, which contains the signal powers information and the mutual coupling information. The problem of off-grid DOAs is also considered for sparse Bayesian compressive sensing. Based on the estimated vector, a peak searching is performed to estimate the initial DOA. Finally, the estimation of DOA is modified to initial estimate plus off-grid error value. The advantage of fully utilizing the degree of freedom of nested arrays is preserved in this proposed algorithm. Moreover, no complicated calculation is needed to obtain the mutual coupling coefficients or rearrange the position of array element. Theoretical analysis and simulation results show the effectiveness of the proposed algorithm.

关键词： direction of arrival estimation mutual coupling nested array off-grid model sparse Bayesian learning method

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SPARSE BAYESIAN LEARNING FOR DOA ESTIMATION USING CO-PRIME AND nested arrayS 10

SPARSE BAYESIAN LEARNING FOR DOA ESTIMATION USING CO-PRIME A...

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10th IEEE Sensor array and Multichannel Signal Processing Workshop (SAM)

作者： Nannuru, Santosh Gerstoft, Peter Koochakzadeh, Ali Pal, Piya Univ Calif San Diego Scripps Inst Oceanog La Jolla CA 92093 USA Univ Calif San Diego Elect & Comp Engn La Jolla CA 92093 USA

ISBN: (纸本)9781538647523

Sparse Bayesian learning (SBL) has been used to obtain source direction-of-arrivals (DoAs) from uniform linear array (ULA) data. The maximum number of sources that can be resolved using a ULA is limited by the number of sensors in the array. It is known that sparse linear arrays such as co-prime and nested arrays can resolve more sources than the number of sensors. In this paper we demonstrate this using SBL. We compute the mean squared error in source power estimation as various parameters are varied.

关键词： Sparse Bayesian learning co-prime array nested array DoA estimation compressed sensing

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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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A BROADBAND MIMO SENSING SYSTEM WITH COLOCATED nested arrayS: TRANSMIT SCHEME AND DIRECTION-OF-ARRIVAL ESTIMATION 14

A BROADBAND MIMO SENSING SYSTEM WITH COLOCATED NESTED ARRAYS...

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14th IEEE International Conference on Signal Processing (ICSP)

作者： Mao, Linlin Li, Hongbin Gao, Yongchan Chinese Acad Sci Inst Acoust Beijing 100190 Peoples R China Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA Xidian Univ Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9781538646731

In this paper, we aim to improve the degrees of freedom (DOF) and direction-of-arrival (DOA) estimation accuracy for a broadband MIMO sensing system with colocated nested arrays. A new transmit scheme, which decomposes the entire bandwidth used by the broadband MIMO system into multiple sub-bands, is proposed. Multiple pulses are employed to establish cyclic transmission between different transmit antennas so that signals from different antennas can be easily separated in the frequency domain at the receiver. A focusbased coherent DOA estimation method is also developed to make full use of the difference coarray aperture of the MIMO virtual array. Simulation results illustrate the superior performance of the proposed system.

关键词： Broadband multiple-input multiple-output (MIMO) system transmit scheme degrees of freedom (DOF) direction of arrival (DOA) estimation nested array

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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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Real-Valued Direct Position Determination of Quasi-Stationary Signals for nested arrays: Khatri-Rao Subspace and Unitary Transformation

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SENSORS 2022年第11期22卷 4209-4209页

作者： Zeng, Haowei Yue, Heng Cao, Jinke Zhang, Xiaofei Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 211106 Peoples R China Minist Ind & Informat Technol Key Lab Dynam Cognit Syst Electromagnet Spectrum Nanjing 211106 Peoples R China

The features of quasi-stationary signals (QSS) are considered to be in a direct position determination (DPD) framework, and a real-valued DPD algorithm of QSS for nested arrays is proposed. By stacking the vectorization form of the signal's covariance for different frames and further eliminating noise, a new noise-eliminated received signal matrix is obtained first. Then, the combination of the Khatri-Rao subspace method and subspace data fusion method was performed to form the cost function. High complexity can be reduced by matrix reconstruction, including the modification of the dimension-reduced matrix and unitary transformation. Ultimately, the advantage of lower complexity, compared with the previous algorithm, is verified by complexity analysis, and the superiority over the existing algorithms, in terms of the maximum number of identifiable sources, estimation accuracy, and resolution, are corroborated by some simulation results.

关键词： quasi-stationary signals direct position determination nested array Khatri-Rao subspace subspace data fusion dimension-reduced unitary transformation

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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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Optimized Sparse nested arrays for DoA Estimation of Non-circular Signals

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SIGNAL PROCESSING 2023年 204卷

作者： Mohsen, Nabil Hawbani, Ammar Wang, Xingfu Agrawal, Monika Zhao, Liang Univ Sci & Technol China Sch Comp Sci Hefei Peoples R China Indian Inst Technol Delhi Ctr Appl Res Elect New Delhi 110016 India Shenyang Aerosp Univ Shenyang Peoples R China

The recent criteria for designing sparse arrays used in many applications are the number of degrees of freedom (DoFs) and mutual coupling. In this paper, two optimized sparse nested arrays, called OSNAI and OSNA-II, are proposed for the direction of arrival (DoA) estimation of non-circular signals. The proposed structures aim to enhance the DoFs and mitigate mutual coupling. The OSNA-I and OSNA-II are obtained by rearranging physical sensors of the conventional nested array. Specifically, for the proposed OSNA-I, its dense uniform linear array (ULA) is exactly the same as the dense ULA of the conventional nested array, while the physical sensors of its sparse ULA are located at n (2 N 1 + 1) d. For the proposed OSNA-II, it is composed of two subarrays plus one separate sensor that is appropriately placed. The OSNAII keeps the dense array of the conventional nested array untouched except the sensor at N 1 d being removed, while its sparse array is deployed according to n (2 N 1 ) d. The separate sensor is set apart with the rightmost sensor of the sparse array by a space of 3 N 1 d. The closed-form expressions are derived for the actual sensor locations, number of uniform DoFs and available DoFs, respectively. Besides, it is also proven that OSNA-I and OSNA-II have small weight values due to their sparse structures, which means that OSNA-I and OSNA-II can achieve a good balance between DoFs and weight values. Simulation results demonstrate the superiorities of the proposed structures in terms of robustness and estimation accuracy.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Sparse array nested array Non-circular signals Sum-difference coarray Direction-of-arrival estimation Mutual coupling

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