scholarly journals Gridless sparsity-based DOA estimation for sparse linear array

2019 ◽  
Vol 2019 (20) ◽  
pp. 6629-6632
Author(s):  
Yu Zhang ◽  
Gong Zhang ◽  
Yingying Kong ◽  
Fangqing Wen
Keyword(s):  
Linear Array ◽  
Doa Estimation ◽  
Sparse Linear Array

Underdetermined 2D DOA Estimation Based on Bi-directional Parallel Sparse Linear Array with Reduced Mutual Coupling

2021 ◽  
Author(s):  
Geng Wang ◽  
Changxiao Chen ◽  
Yi He ◽  
Mingyue Feng ◽  
Ying Jiang ◽  
...  
Keyword(s):  
Mutual Coupling ◽  
Linear Array ◽  
Doa Estimation ◽  
Sparse Linear Array ◽  
2D Doa Estimation

scholarly journals Design of sparse arrays via deep learning for enhanced DOA estimation

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Steven Wandale ◽  
Koichi Ichige
Keyword(s):  
Deep Learning ◽  
Linear Array ◽  
Antenna Selection ◽  
Computational Cost ◽  
Doa Estimation ◽  
Classification Problem ◽  
Training Data ◽  
Sparse Linear Array ◽  
Selection Approach ◽  
Selection For

AbstractThis paper introduces an enhanced deep learning-based (DL) antenna selection approach for optimum sparse linear array selection for direction-of-arrival (DOA) estimation applications. Generally, the antenna selection problem yields a combination of subarrays as a solution. Previous DL-based methods designated these subarrays as classes to fit the problem into a classification problem to which a convolutional neural network (CNN) is employed to solve it. However, these methods sample the combination set randomly to reduce computational cost related to the generation of training data, and it often leads to sub-optimal solutions due to ill-sampling issues. Hence, in this paper, we propose an improved DL-based method by constraining the combination set to retain the hole-free subarrays to enhance the method’s performance and sparse subarrays rendered. Numerical examples show that the proposed method yields sparser subarrays with better beampattern properties and improved DOA estimation performance than conventional DL techniques.

scholarly journals Sparse linear array with low mutual coupling ratio for DOA estimation

2021 ◽  
Vol 15 (6) ◽  
pp. 866-873
Author(s):  
Fan Wu ◽  
Fei Cao ◽  
Xiaowei Feng ◽  
Xiaogang Ni ◽  
Chong Chen
Keyword(s):  
Mutual Coupling ◽  
Linear Array ◽  
Doa Estimation ◽  
Coupling Ratio ◽  
Sparse Linear Array

Comparison between DOA Estimation Subspace methods for incoherent and coherent signals

2015 ◽  
Vol 1 ◽  
pp. 18
Author(s):  
Ahmed Abdalla ◽  
Suhad Mohammed ◽  
Tang Bin ◽  
Jumma Mary Atieno ◽  
Abdelazeim Abdalla
Keyword(s):  
Linear Array ◽  
Doa Estimation ◽  
Far Field ◽  
Matlab Simulation ◽  
Subspace Methods ◽  
Coherent Signals ◽  
Uniform Linear Array ◽  
Pros And Cons ◽  
Basic Concepts ◽  
And Performance

This paper considers the problem of estimating the direction of arrival (DOA) for the both incoherent and coherent signals from narrowband sources, located in the far field in the case of uniform linear array sensors. Three different methods are analyzed. Specifically, these methods are Music, Root-Music and ESPRIT. The pros and cons of these methods are identified and compared in light of different viewpoints. The performance of the three methods is evaluated, analytically, when possible, and by Matlab simulation. This paper can be a roadmap for beginners in understanding the basic concepts of DOA estimation issues, properties and performance.

scholarly journals Three-Dimensional Imaging of Terahertz Circular SAR with Sparse Linear Array

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2477 ◽  
Author(s):  
Jubo Hao ◽  
Jin Li ◽  
Yiming Pi
Keyword(s):  
3D Imaging ◽  
Linear Array ◽  
Ground Plane ◽  
Three Dimensional ◽  
Terahertz Waves ◽  
Recovery Method ◽  
Imaging Algorithm ◽  
Imaging Geometry ◽  
Imaging Results ◽  
Sparse Linear Array

Due to the non-contact detection ability of radar and the harmlessness of terahertz waves to the human body, three-dimensional (3D) imaging using terahertz synthetic aperture radar (SAR) is an efficient method of security detection in public areas. To achieve high-resolution and all aspect imaging, circular trajectory movement of radar and linear sensor array along the height direction were used in this study. However, the short wavelength of terahertz waves makes it practically impossible for the hardware to satisfy the half-wavelength spacing condition to avoid grating lobes. To solve this problem, a sparse linear array model based on the equivalent phase center principle was established. With the designed imaging geometry and corresponding echo signal model, a 3D imaging algorithm was derived. Firstly, the phase-preserving algorithm was adopted to obtain the 2D image of the ground plane for each sensor. Secondly, the sparse recovery method was applied to accomplish the scattering coefficient reconstruction along the height direction. After reconstruction of all the range-azimuth cells was accomplished, the final 3D image was obtained. Numerical simulations and experiments using terahertz radar were performed. The imaging results verify the effectiveness of the 3D imaging algorithm for the proposed model and validate the feasibility of terahertz radar applied in security detection.

Localisation of mixed near‐field and far‐field sources using the largest aperture sparse linear array

2018 ◽  
Vol 12 (2) ◽  
pp. 155-162 ◽  
Author(s):  
Ali Akbar Ebrahimi ◽  
Hamid Reza Abutalebi ◽  
Mahmood Karimi
Keyword(s):  
Linear Array ◽  
Near Field ◽  
Far Field ◽  
Sparse Linear Array

scholarly journals DOA Estimation for Coprime Linear Array Based on MI-ESPRIT and Lookup Table

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3043 ◽  
Author(s):  
Weike Zhang ◽  
Xi Chen ◽  
Kaibo Cui ◽  
Tao Xie ◽  
Naichang Yuan
Keyword(s):  
Linear Array ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Spatial Spectrum ◽  
Angle Measurement ◽  
Lookup Table ◽  
Signal Parameters ◽  
Estimation Precision ◽  
Simulation Results ◽  
The Relationship

In order to improve the angle measurement performance of a coprime linear array, this paper proposes a novel direction-of-arrival (DOA) estimation algorithm for a coprime linear array based on the multiple invariance estimation of signal parameters via rotational invariance techniques (MI-ESPRIT) and a lookup table method. The proposed algorithm does not require a spatial spectrum search and uses a lookup table to solve ambiguity, which reduces the computational complexity. To fully use the subarray elements, the DOA estimation precision is higher compared with existing algorithms. Moreover, the algorithm avoids the matching error when multiple signals exist by using the relationship between the signal subspace of two subarrays. Simulation results verify the effectiveness of the proposed algorithm.

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