Sparse off-grid DOA estimation method with unknown mutual coupling effect

2019 ◽  
Vol 90 ◽  
pp. 1-9 ◽  
Author(s):  
Peng Chen ◽  
Zhenxin Cao ◽  
Zhimin Chen ◽  
Chunhua Yu
Keyword(s):  
Mutual Coupling ◽  
Coupling Effect ◽  
Estimation Method ◽  
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.

scholarly journals Computationally Efficient 2D DOA Estimation for L-Shaped Array with Unknown Mutual Coupling

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yang-Yang Dong ◽  
Xin Chang
Keyword(s):  
Mutual Coupling ◽  
Coupling Effect ◽  
Estimation Method ◽  
Doa Estimation ◽  
Correlation Effect ◽  
Complex Eigenvalue ◽  
Computationally Efficient ◽  
Angle Estimation ◽  
Estimation Performance ◽  
2D Doa Estimation

Although L-shaped array can provide good angle estimation performance and is easy to implement, its two-dimensional (2D) direction-of-arrival (DOA) performance degrades greatly in the presence of mutual coupling. To deal with the mutual coupling effect, a novel 2D DOA estimation method for L-shaped array with low computational complexity is developed in this paper. First, we generalize the conventional mutual coupling model for L-shaped array and compensate the mutual coupling blindly via sacrificing a few sensors as auxiliary elements. Then we apply the propagator method twice to mitigate the effect of strong source signal correlation effect. Finally, the estimations of azimuth and elevation angles are achieved simultaneously without pair matching via the complex eigenvalue technique. Compared with the existing methods, the proposed method is computationally efficient without spectrum search or polynomial rooting and also has fine angle estimation performance for highly correlated source signals. Theoretical analysis and simulation results have demonstrated the effectiveness of the proposed method.

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 DOA Estimation Using Fourth-Order Cumulants in Nested Arrays with Structured Imperfections

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 994
Author(s):  
Baoping Wang ◽  
Junhao Zheng
Keyword(s):  
Fourth Order ◽  
Mutual Coupling ◽  
Estimation Method ◽  
Doa Estimation ◽  
Coupling Model ◽  
Coupling Coefficients ◽  
Rank Reduction ◽  
Phase Errors ◽  
Reduction Property ◽  
Nested Arrays

Recently developed super nested array families have drawn much attention owing to their merits on keeping the benefits of the standard nested arrays while further mitigating coupling in dense subarray portions. In this communication, a new mutual coupling model for nested arrays is constructed. Analyzing the structure of the newly formed mutual coupling matrix, a transformation of the distorted steering vector to separate angular information from the mutual coupling coefficients is revealed. By this property, direction of arrival (DOA) estimates can be determined via a grid search for the minimum of a determinant function of DOA, which is induced by the rank reduction property. We also extend the robust DOA estimation method to accommodate the unknown mutual coupling and gain-phase mismatches in the nested array. Compared with the schemes of super nested array families on reducing the mutual coupling effects, the solutions presented in this paper has two advantages: (a) It is applicable to the standard nested arrays without rearranging the configuration to increase the inter-element spacing, alleviating the cross talk in dense uniform linear arrays (ULAs) as well as gain-phase errors in sparse ULA parts; (b) Perturbations in nested arrays are estimated in colored noise, which is significant but rarely discussed before. Simulations results corroborate the superiority of the proposed methods using fourth-order cumulants.

scholarly journals Mutual Coupling in Phased Arrays: A Review

2013 ◽  
Vol 2013 ◽  
pp. 1-23 ◽  
Author(s):  
Hema Singh ◽  
H. L. Sneha ◽  
R. M. Jha
Keyword(s):  
Mutual Coupling ◽  
Phased Arrays ◽  
Coupling Effect ◽  
Doa Estimation ◽  
Interference Rejection ◽  
Radiation Characteristics ◽  
Resolution Capability ◽  
Different Types ◽  
Signal Environment ◽  
Array Performance

The mutual coupling between antenna elements affects the antenna parameters like terminal impedances, reflection coefficients and hence the antenna array performance in terms of radiation characteristics, output signal-to-interference noise ratio (SINR), and radar cross section (RCS). This coupling effect is also known to directly or indirectly influence the steady state and transient response, the resolution capability, interference rejection, and direction-of-arrival (DOA) estimation competence of the array. Researchers have proposed several techniques and designs for optimal performance of phased array in a given signal environment, counteracting the coupling effect. This paper presents a comprehensive review of the methods that model and mitigate the mutual coupling effect for different types of arrays. The parameters that get affected due to the presence of coupling thereby degrading the array performance are discussed. The techniques for optimization of the antenna characteristics in the presence of coupling are also included.

scholarly journals An Efficient 2-D DOA Estimation for a Cylindrical Conformal Array with Unknown Mutual Coupling

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Chao Liu ◽  
Shunian Yin
Keyword(s):  
Mutual Coupling ◽  
Coupling Effect ◽  
Doa Estimation ◽  
Coupling Coefficients ◽  
Rotation Invariant ◽  
Rank Reduction ◽  
Estimation Algorithms ◽  
Conformal Array ◽  
Signal Parameters ◽  
Finding Method

The limited space of a conformal array may lead to a serious mutual coupling effect, which will significantly affect the performance of direction of arrival (DOA) estimation algorithms. In this paper, an efficient 2-D direction finding method is developed in the presence of unknown mutual coupling for the uniform cylindrical conformal array (CCA). To avoid the time-consuming two-dimensional spectral peak searching, the 2-D DOA estimation is decoupled and divided into two 1-D DOA estimations. Elevation is first estimated based on a subarray estimation of signal parameters via rotation invariant technique (ESPRIT), and then azimuth is estimated based on the rank reduction (RARE) method by using the elevation estimation result. Consequently, the mutual coupling coefficients can be estimated after getting the DOA estimates. The proposed method can well calibrate the mutual coupling effect of a cylindrical array with a low computational complexity. The final simulation results corroborate our analysis.

scholarly journals The Design of A Novel Sparse Array Using Two Uniform Linear Arrays considering Mutual Coupling

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Haiyun Xu ◽  
Weijia Cui ◽  
Fengtong Mei ◽  
Bin Ba ◽  
Chunxiao Jian
Keyword(s):  
Mutual Coupling ◽  
Coupling Effect ◽  
Design Method ◽  
Doa Estimation ◽  
Sensor Failure ◽  
Linear Arrays ◽  
Sparse Array ◽  
The Relationship ◽  
Analytical Expressions ◽  
Selection Of

The sparse arrays using two uniform linear arrays have attracted considerable interest due to the capability of giving analytical expression of sensor location and owning robust direction-of-arrival (DOA) facing strong mutual coupling and sensor failure. In order to achieve the maximum consecutive virtual uniform linear array in difference coarray, in this paper, a design method of a novel sparse array using two uniform linear arrays (NSA-U2) is proposed. We first analyze the relationship between the values of displacement of two subarrays and difference coarray, and then we give the analytical expressions of the displacement and the number of consecutive lags. By discussing the selection of number of subarray sensors, the design of NSA-U2 is completed. Moreover, through choosing a proper compressed interelement spacing, NSA-U2 can be robust to mutual coupling effect. Numerical experiments prove the effectiveness and favorable performance of DOA estimation with mutual coupling.

scholarly journals Sparse DOD/DOA Estimation in a Bistatic MIMO Radar With Mutual Coupling Effect

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 341 ◽  
Author(s):  
Peng Chen ◽  
Zhenxin Cao ◽  
Zhimin Chen ◽  
Chunhua Yu
Keyword(s):  
Mutual Coupling ◽  
Coupling Effect ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Joint Estimation ◽  
Reconstruction Method ◽  
Estimation Model ◽  
Unknown Parameters ◽  
Estimation Performance ◽  
Direction Of Departure

The unknown mutual coupling effect between antennas significantly degrades the target localization performance in the bistatic multiple-input multiple-output (MIMO) radar. In this paper, the joint estimation problem for the direction of departure (DOD) and direction of arrival (DOA) is addressed. By exploiting the target sparsity in the spatial domain and formulating a dictionary matrix with discretizing the DOD/DOA into grids, compressed sensing (CS)-based system model is given. However, in the practical MIMO radar systems, the target cannot be precisely on the grids, and the unknown mutual coupling effect degrades the estimation performance. Therefore, a novel CS-based DOD/DOA estimation model with both the off-grid and mutual coupling effect is proposed, and a novel sparse reconstruction method is proposed to estimate DOD/DOA with updating both the off-grid and mutual coupling parameters iteratively. Moreover, to describe the estimation performance, the corresponding Cramér–Rao lower bounds (CRLBs) with all the unknown parameters are theoretically derived. Simulation results show that the proposed method can improve the DOD/DOA estimation in the scenario with unknown mutual coupling effect, and outperform state-of-the-art methods.

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