A study of the effects of mutual coupling on the direction-finding performance of a linear array using the methods of moments

2003 ◽  
Author(s):  
D.H. Shau ◽  
A.T. Adams ◽  
T.K. Sarkar
Keyword(s):  
Mutual Coupling ◽  
Linear Array ◽  
Direction Finding ◽  
Methods Of Moments

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.

scholarly journals Mutual Coupling Compensation for Direction-of-Arrival Estimations Using the Receiving-Mutual-Impedance Method

2010 ◽  
Vol 2010 ◽  
pp. 1-7 ◽  
Author(s):  
Hoi Shun Lui ◽  
Hon Tat Hui
Keyword(s):  
Mutual Coupling ◽  
Linear Array ◽  
Direction Of Arrival ◽  
Short Review ◽  
Direction Finding ◽  
Impedance Method ◽  
Mutual Impedance ◽  
Determination Methods

A short review of the receiving-mutual-impedance method (RMIM) for mutual coupling compensation in direction finding applications using linear array is conducted. The differences between the conventional-mutual-impedance method (CMIM) and RMIM, as well as the three different determination methods for receiving mutual impedance (RMI), will be discussed in details. As an example, direction finding with better accuracies is used for demonstrating the superiority of mutual coupling compensation using RMIM.

scholarly journals How Varying the Dipole Lengths of a Uniform Linear Array Affects the Performance of an ESPRIT based Direction Finding Algorithm

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Method Of Moments ◽  
Mutual Coupling ◽  
Linear Array ◽  
Direction Of Arrival ◽  
Direction Finding ◽  
Direction Of Arrival Estimation ◽  
Simulation Tool ◽  
Uniform Linear Array ◽  
Coupling Matrix ◽  
Esprit Algorithm

Popularly used to collect data for the direction-of-arrival estimation of an incident source. Since the dipole elements of the uniform linear array are electromagnetically and mutually coupled, the presence of mutual coupling may degrade the performance of any direction finding algorithms. In order to mitigate mutual coupling, several references introduced modifications of the well-known ESPRIT algorithm. These modifications involve discarding the data collected by linear array at the two ends. However, the assumption of several literature that the mutual coupling matrix to be both Toeplitz and banded are invalid. This is pointed out by the “method of moments” based computer simulation tool used in this paper. The Toeplitz-and-banded coupling-matrix assumption leads to the mutual coupling being mis-modeled. Furthermore, previous studies failed to consider how varying the dipoles’ electrical length affect the performance of the direction-of-arrival estimation under mutual coupling.

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.

Direction Finding by Time-Modulated Linear Array

2018 ◽  
Vol 66 (7) ◽  
pp. 3642-3652 ◽  
Author(s):  
Chong He ◽  
Anjie Cao ◽  
Jingfeng Chen ◽  
Xianling Liang ◽  
Weiren Zhu ◽  
...  
Keyword(s):  
Linear Array ◽  
Direction Finding

scholarly journals Direction finding and mutual coupling estimation for uniform rectangular arrays

2015 ◽  
Vol 117 ◽  
pp. 61-68 ◽  
Author(s):  
Han Wu ◽  
Chunping Hou ◽  
Hua Chen ◽  
Wei Liu ◽  
Qing Wang
Keyword(s):  
Mutual Coupling ◽  
Direction Finding ◽  
Rectangular Arrays
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