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

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 Compressed Sensing-Based DOA Estimation with Unknown Mutual Coupling Effect

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 424 ◽  
Author(s):  
Peng Chen ◽  
Zhenxin Cao ◽  
Zhimin Chen ◽  
Linxi Liu ◽  
Man Feng
Keyword(s):  
Compressed Sensing ◽  
Mutual Coupling ◽  
Linear Array ◽  
State Of The Art ◽  
Coupling Effect ◽  
Estimation Method ◽  
Doa Estimation ◽  
System Model ◽  
Reconstruction Problem ◽  
Uniform Linear Array

The performance of a direction-finding system is significantly degraded by the imperfection of an array. In this paper, the direction-of-arrival (DOA) estimation problem is investigated in the uniform linear array (ULA) system with the unknown mutual coupling (MC) effect. The system model with MC effect is formulated. Then, by exploiting the signal sparsity in the spatial domain, a compressed-sensing (CS)-based system model is proposed with the MC coefficients, and the problem of DOA estimation is converted into that of a sparse reconstruction. To solve the reconstruction problem efficiently, a novel DOA estimation method, named sparse-based DOA estimation with unknown MC effect (SDMC), is proposed, where both the sparse signal and the MC coefficients are estimated iteratively. Simulation results show that the proposed method can achieve better performance of DOA estimation in the scenario with MC effect than the state-of-the-art methods, and improve the DOA estimation performance about 31.64 % by reducing the MC effect by about 4 dB.

Mutual Coupling Compensation in Receiving Arrays and Its Implementation on Software Defined Radios

2021 ◽  
Vol 35 (11) ◽  
pp. 1433-1434
Author(s):  
Sana Khan ◽  
Hassan Sajjad ◽  
Mehmet Ozdemir ◽  
Ercument Arvas
Keyword(s):  
Real Time ◽  
Mutual Coupling ◽  
Linear Array ◽  
Incident Angle ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Substantial Improvement ◽  
Uniform Linear Array ◽  
Low Snr ◽  
Software Defined Radios

Mutual coupling is compensated in a four element uniform linear receiving array using software defined radios. Direction of arrival (DoA) is estimated in real-time for the array with spacing d=lambda/4. The decoupling matrix was measured using a VNA for only one incident angle. After compensation the error in DoA estimation was reduced to 5%. Comparing the DoA results with d=lambda/2 spaced Uniform Linear Array (ULA), 1.2% error was observed. Although, the experiment was performed indoors with a low SNR, the results show a substantial improvement in the estimated DoA after compensation.

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 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

Real-valued DOA estimation for uniform linear array with unknown mutual coupling

2012 ◽  
Vol 92 (9) ◽  
pp. 2056-2065 ◽  
Author(s):  
Jisheng Dai ◽  
Weichao Xu ◽  
Dean Zhao
Keyword(s):  
Mutual Coupling ◽  
Linear Array ◽  
Doa Estimation ◽  
Uniform Linear Array
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