Efficient phase-only linear array synthesis including mutual coupling and platform effect

2016 ◽  
Author(s):  
Xin Huang ◽  
Yanhui Liu ◽  
Pengfei You ◽  
Jing Yang ◽  
Qing Huo Liu
Keyword(s):  
Mutual Coupling ◽  
Linear Array ◽  
Platform Effect

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.

scholarly journals SBL-Based Direction Finding Method with Imperfect Array

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 426 ◽  
Author(s):  
Peng Chen  ◽  
Zhimin Chen ◽  
Xuan Zhang ◽  
Linxi Liu
Keyword(s):  
Mutual Coupling ◽  
Bayesian Learning ◽  
Linear Array ◽  
State Of The Art ◽  
Coupling Effect ◽  
Direction Finding ◽  
Noise Variance ◽  
Uniform Linear Array ◽  
Unknown Parameters ◽  
Finding Method

The imperfect array degrades the direction finding performance. In this paper, we investigate the direction finding problem in uniform linear array (ULA) system with unknown mutual coupling effect between antennas. By exploiting the target sparsity in the spatial domain, the sparse Bayesian learning (SBL)-based model is proposed and converts the direction finding problem into a sparse reconstruction problem. In the sparse-based model, the off-grid errors are introduced by discretizing the direction area into grids. Therefore, an off-grid SBL model with mutual coupling vector is proposed to overcome both the mutual coupling and the off-grid effect. With the distribution assumptions of unknown parameters including the noise variance, the off-grid vector, the received signals and the mutual coupling vector, a novel direction finding method based on SBL with unknown mutual coupling effect named DFSMC is proposed, where an expectation-maximum (EM)-based step is adopted by deriving the estimation expressions for all the unknown parameters theoretically. Simulation results show that the proposed DFSMC method can outperform state-of-the-art direction finding methods significantly in the array system with unknown mutual coupling effect.

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.

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 Mutual Coupling Reduction and Pattern Error Correction in a 5G Beamforming Linear Array Using CSRR

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 65922-65934 ◽  
Author(s):  
Raghuraman Selvaraju ◽  
Mohd Haizal Jamaluddin ◽  
Muhammad Ramlee Kamarudin ◽  
Jamal Nasir ◽  
Muhammad Hashim Dahri
Keyword(s):  
Error Correction ◽  
Mutual Coupling ◽  
Linear Array ◽  
Mutual Coupling Reduction
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