scholarly journals 2D DOA Estimation with a Two-Parallel Array Consisting of Two Uniform Large-Spacing Linear Arrays

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Sheng Liu ◽  
Jing Zhao ◽  
Yu Zhang
Keyword(s):  
Mutual Coupling ◽  
Elevation Angle ◽  
Doa Estimation ◽  
Matrix Product ◽  
Linear Arrays ◽  
Parallel Array ◽  
Propagator Method ◽  
The Matrix ◽  
2D Doa Estimation ◽  
Estimation Precision

In this paper, an improved propagator method (PM) is proposed by using a two-parallel array consisting of two uniform large-spacing linear arrays. Because of the increase of element spacing, the mutual coupling between two sensors can be reduced. Firstly, two matrices containing elevation angle information are obtained by PM. Then, by performing EVD of the product of the two matrices, the elevation angles of incident signals can be estimated without direction ambiguity. At last, the matrix product is used again to obtain the estimations of azimuth angles. Compared with the existed PM algorithms based on conventional uniform two-parallel linear array, the proposed PM algorithm based on the large-spacing linear arrays has higher estimation precision. Many simulation experiments are presented to verify the effect of proposed scheme in reducing the mutual coupling and improving estimation precision.

scholarly journals Enhanced 2D-DOA Estimation for Large Spacing Three-Parallel Uniform Linear Arrays

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Dong Zhang ◽  
Yongshun Zhang ◽  
Cunqian Feng
Keyword(s):  
Elevation Angle ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Low Complexity ◽  
Estimation Accuracy ◽  
Linear Arrays ◽  
Eigen Value ◽  
Directional Cosine ◽  
2D Doa Estimation ◽  
Value Decomposition

An enhanced two-dimensional direction of arrival (2D-DOA) estimation algorithm for large spacing three-parallel uniform linear arrays (ULAs) is proposed in this paper. Firstly, we use the propagator method (PM) to get the highly accurate but ambiguous estimation of directional cosine. Then, we use the relationship between the directional cosine to eliminate the ambiguity. This algorithm not only can make use of the elements of the three-parallel ULAs but also can utilize the connection between directional cosine to improve the estimation accuracy. Besides, it has satisfied estimation performance when the elevation angle is between 70° and 90° and it can automatically pair the estimated azimuth and elevation angles. Furthermore, it has low complexity without using any eigen value decomposition (EVD) or singular value decompostion (SVD) to the covariance matrix. Simulation results demonstrate the effectiveness of our proposed algorithm.

Modified Propagator Method for 2-Dimensional DOA Estimation in Monostatic MIMO Radar with L-Shaped Array

2013 ◽  
Vol 846-847 ◽  
pp. 1171-1175
Author(s):  
Xin Li ◽  
Ding Jie Xu ◽  
Xiao Meng Wang
Keyword(s):  
Doa Estimation ◽  
Mimo Radar ◽  
Complex Matrix ◽  
Linear Arrays ◽  
Complex Eigenvalues ◽  
Propagator Method ◽  
The Matrix ◽  
Esprit Algorithm ◽  
Influence Of Noise ◽  
Better Than

A modified propagator method based on L-shaped array for 2-Dimensional (2-D) direction of arrival (DOA) estimation in monostatic MIMO radar is proposed. A cross-correlation matrix, which can eliminate the influence of noise, is constructed by the received data from the two orthogonal uniform linear arrays (ULAs) at x-axis and z-axis. Then the matrix can be utilized to estimate signal subspace of 2-D DOA through propagator method. At last, the elevation and azimuth angles of the 2-D DOA is automatically paired by the complex eigenvalues of a low-order complex matrix. The 2-D DOA estimation performance of the proposed method is better than conventional propagator method and ESPRIT algorithm. Simulation results verify the effectiveness of the proposed method.

scholarly journals Efficient Two-Dimensional Direction Finding Algorithm for Rectilinear Sources Under Unknown Mutual Coupling

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1914
Author(s):  
Jian Xie ◽  
Qiuping Wang ◽  
Yuexian Wang ◽  
Xin Yang
Keyword(s):  
Degrees Of Freedom ◽  
Mutual Coupling ◽  
Array Signal Processing ◽  
Elevation Angle ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Rank Reduction ◽  
Communication Signals ◽  
Estimation Performance ◽  
Direction Cosines

Digital communication signals in wireless systems may possess noncircularity, which can be used to enhance the degrees of freedom for direction-of-arrival (DOA) estimation in sensor array signal processing. On the other hand, the electromagnetic characteristics between sensors in uniform rectangular arrays (URAs), such as mutual coupling, may significantly deteriorate the estimation performance. To deal with this problem, a robust real-valued estimator for rectilinear sources was developed to alleviate unknown mutual coupling in URAs. An augmented covariance matrix was built up by extracting the real and imaginary parts of observations containing the circularity and noncircularity of signals. Then, the actual steering vector considering mutual coupling was reparameterized to make the rank reduction (RARE) property available. To reduce the computational complexity of two-dimensional (2D) spectral search, we individually estimated y-axis and x-axis direction-cosines in two stages following the principle of RARE. Finally, azimuth and elevation angle estimates were determined from the corresponding direction-cosines respectively. Compared with existing solutions, the proposed method is more computationally efficient, involving real-valued operations and decoupled 2D spectral searches into twice those of one-dimensional searches. Simulation results verified that the proposed method provides satisfactory estimation performance that is robust to unknown mutual coupling and close to the counterparts based on 2D spectral searches, but at the cost of much fewer calculations.

DOA Estimation Under Mutual Coupling of Uniform Linear Arrays Using Sparse Reconstruction

2019 ◽  
Vol 8 (4) ◽  
pp. 1004-1007 ◽  
Author(s):  
Yuexian Wang ◽  
Ling Wang ◽  
Jian Xie ◽  
Matthew Trinkle ◽  
Brian W.-H. Ng
Keyword(s):  
Mutual Coupling ◽  
Doa Estimation ◽  
Sparse Reconstruction ◽  
Linear Arrays

scholarly journals 2D-DOA Estimation for Cylindrical Array with Mutual Coupling

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Hao Feng ◽  
Lutao Liu ◽  
Biyang Wen
Keyword(s):  
Mutual Coupling ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Coupling Matrix ◽  
Estimation Algorithms ◽  
Conformal Array ◽  
Signal Parameters ◽  
2D Doa Estimation ◽  
Two Parameter ◽  
Cylindrical Array

Most conventional direction-of-arrival (DOA) estimation algorithms are affected by the effect of mutual coupling, which make the performance of DOA estimation degrade. In this paper, a novel DOA estimation algorithm for conformal array in the presence of unknown mutual coupling is proposed. The special mutual coupling matrix (MCM) is applied to eliminate the effect of mutual coupling. With suitable array design, the decoupling between polarization parameter and angle information is accomplished. The two-demission DOA (2D-DOA) estimation is finally achieved based on estimation of signal parameters via rotational invariance techniques (ESPRIT). The proposed algorithm can be extended to conical conformal array as well. Two parameter pairing methods are illustrated for cylindrical and conical conformal array, respectively. The computer simulation verifies the effectiveness of the proposed algorithm.

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