Effects of Mutual Coupling and Array Dissimilarity on Angle Estimation in MIMO Radar

2019 ◽  
Author(s):  
Abdul Azim Azhar ◽  
Nur Emileen Abd Rashid ◽  
Mohd Khairil Azhar Mahmood ◽  
Idnin Pasya
Keyword(s):  
Mutual Coupling ◽  
Mimo Radar ◽  
Angle Estimation

scholarly journals Tensor-Based Angle Estimation Approach for Strictly Noncircular Sources with Unknown Mutual Coupling in Bistatic MIMO Radar

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2788 ◽  
Author(s):  
Yuehao Guo ◽  
Xianpeng Wang ◽  
Wensi Wang ◽  
Mengxing Huang ◽  
Chong Shen ◽  
...  
Keyword(s):  
Mutual Coupling ◽  
Multiple Input Multiple Output ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Angle Estimation ◽  
Estimation Performance ◽  
Direction Of Departure ◽  
Value Decomposition

In the paper, the estimation of joint direction-of-departure (DOD) and direction-of-arrival (DOA) for strictly noncircular targets in multiple-input multiple-output (MIMO) radar with unknown mutual coupling is considered, and a tensor-based angle estimation method is proposed. In the proposed method, making use of the banded symmetric Toeplitz structure of the mutual coupling matrix, the influence of the unknown mutual coupling is removed in the tensor domain. Then, a special enhancement tensor is formulated to capture both the noncircularity and inherent multidimensional structure of strictly noncircular signals. After that, the higher-order singular value decomposition (HOSVD) technology is applied for estimating the tensor-based signal subspace. Finally, the direction-of-departure (DOD) and direction-of-arrival (DOA) estimation is obtained by utilizing the rotational invariance technique. Due to the use of both noncircularity and multidimensional structure of the detected signal, the algorithm in this paper has better angle estimation performance than other subspace-based algorithms. The experiment results verify that the method proposed has better angle estimation performance.

scholarly journals Angle Estimation Using Local Searching for Bistatic MIMO Radar with Unknown MCM

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Chaochen Tang ◽  
Hongbing Qiu ◽  
Xin Liu ◽  
Qinghua Tang
Keyword(s):  
Computational Complexity ◽  
Local Search ◽  
Mutual Coupling ◽  
Computational Cost ◽  
Estimation Method ◽  
Mimo Radar ◽  
Joint Estimation ◽  
Search Range ◽  
Angle Estimation ◽  
Multiple Input

Multiple input and multiple output (MIMO) radar systems have advantages over traditional phased-array radar in resolution, parameter identifiability, and target detection. However, the estimation performance of the direction of arrivals (DOAs) and the direction of departures (DODs) will be significantly degraded for a colocated MIMO radar system with unknown mutual coupling matrix (MCM). Although auxiliary sensors (AS) can be set to solve this problem, the computational cost of two-dimensional multiple signal classification (2D-MUSIC) is still large. In this paper, a new angle estimation method is proposed to reduce the computational complexity. First, a local-search range is defined for each initial angle estimation obtained by the MUSIC with AS method. Second, the new estimation of DOAs and DODs of the targets is estimated via the joint estimation theory of angle and mutual coupling coefficient in the local search area. Simulation results validate that the proposed method can obtain the same precision and have the advantage over the global searching in computational complexity.

scholarly journals Root-MUSIC Based Angle Estimation for MIMO Radar with Unknown Mutual Coupling

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jianfeng Li ◽  
Xiaofei Zhang ◽  
Weiyang Chen
Keyword(s):  
Mutual Coupling ◽  
Estimation Error ◽  
Multiple Input Multiple Output ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Angle Estimation ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Lower Complexity ◽  
Toeplitz Structure

Direction of arrival (DOA) estimation problem for multiple-input multiple-output (MIMO) radar with unknown mutual coupling is studied, and an algorithm for the DOA estimation based on root multiple signal classification (MUSIC) is proposed. Firstly, according to the Toeplitz structure of the mutual coupling matrix, output data of some specified sensors are selected to eliminate the influence of the mutual coupling. Then the reduced-dimension transformation is applied to make the computation burden lower as well as obtain a Vandermonde structure of the direction matrix. Finally, Root-MUSIC can be adopted for the angle estimation. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm and MUSIC-like algorithm. Furthermore, the proposed algorithm has lower complexity than them. The simulation results verify the effectiveness of the algorithm, and the theoretical estimation error of the algorithm is also derived.

Angle Estimation for Bistatic MIMO Radar with Unknown Mutual Coupling Based on Improved Propagator Method

2014 ◽  
Vol 513-517 ◽  
pp. 3029-3033 ◽  
Author(s):  
Jian Feng Li ◽  
Wei Yang Chen ◽  
Xiao Fei Zhang
Keyword(s):  
Mutual Coupling ◽  
Signal To Noise Ratio ◽  
Multiple Input Multiple Output ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Rotational Invariance ◽  
Angle Estimation ◽  
Direction Of Departure ◽  
Input Multiple Output ◽  
Joint Direction

In this paper, joint direction of departure (DOD) and direction of arrival (DOA) estimation for multiple-input multiple-output (MIMO) radar with unknown mutual coupling is studied. An improved propagator calculation method is proposed to overcome the performance degradation problem when signal to noise ratio (SNR) is low. Thereafter, according to the Toeplitz structure of the mutual coupling matrix, the rotational invariance can be extracted for the angle estimation regardless of the mutual coupling from the augmented propagator matrix. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm and conventional PM-like method, and angles are automatically paired. The simulation results verify the effectiveness of the algorithm.

Tensor-based real-valued subspace approach for angle estimation in bistatic MIMO radar with unknown mutual coupling

2015 ◽  
Vol 116 ◽  
pp. 152-158 ◽  
Author(s):  
Xianpeng Wang ◽  
Wei Wang ◽  
Jing Liu ◽  
Qi Liu ◽  
Ben Wang
Keyword(s):  
Mutual Coupling ◽  
Mimo Radar ◽  
Angle Estimation ◽  
Bistatic Mimo Radar

scholarly journals Joint Angles and Mutual Coupling Estimation Algorithm for Bistatic MIMO Radar

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Jianfeng Li ◽  
Xiaofei Zhang
Keyword(s):  
Mutual Coupling ◽  
Multiple Input Multiple Output ◽  
Computational Cost ◽  
Estimation Algorithm ◽  
Mimo Radar ◽  
Coupling Coefficients ◽  
Angle Estimation ◽  
One Dimensional ◽  
Input Multiple Output ◽  
The One

We study the problem of angle estimation for a bistatic multiple-input multiple-output (MIMO) radar with unknown mutual coupling and proposed a joint algorithm for angles and mutual coupling estimation with the characteristics of uniform linear arrays and subspaces exploitation. We primarily obtain an initial estimate of DOA and DOD, then employ the local one-dimensional searching to estimate exactly DOA and DOD, and finally evaluate the parameters of mutual coupling coefficients via the estimated angles. Exploiting twice of the one-dimensional local searching, our method has much lower computational cost than the algorithm in (Liu and Liao (2012)), and automatically obtains the paired two-dimensional angle estimation. Slightly better performance for angle estimation has been achieved via our scheme in contrast to (Liu and Liao (2012)), while the two methods indicate very close performance of mutual coupling estimation. The simulation results verify the algorithmic effectiveness of our scheme.

Angle estimation and mutual coupling self-calibration for ULA-based bistatic MIMO radar

2018 ◽  
Vol 144 ◽  
pp. 61-67 ◽  
Author(s):  
Fangqing Wen ◽  
Zijing Zhang ◽  
Ke Wang ◽  
Guanqun Sheng ◽  
Gong Zhang
Keyword(s):  
Mutual Coupling ◽  
Mimo Radar ◽  
Angle Estimation ◽  
Bistatic Mimo Radar ◽  
Self Calibration
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