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 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 Two-Dimensional Direction of Arrival (DOA) Estimation for Rectangular Array via Compressive Sensing Trilinear Model

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Huaxin Yu ◽  
Xiaofeng Qiu ◽  
Xiaofei Zhang ◽  
Chenghua Wang ◽  
Gang Yang
Keyword(s):  
Compressive Sensing ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Rectangular Array ◽  
Angle Estimation ◽  
Estimation Performance ◽  
Trilinear Model ◽  
2D Doa Estimation

We investigate the topic of two-dimensional direction of arrival (2D-DOA) estimation for rectangular array. This paper links angle estimation problem to compressive sensing trilinear model and derives a compressive sensing trilinear model-based angle estimation algorithm which can obtain the paired 2D-DOA estimation. The proposed algorithm not only requires no spectral peak searching but also has better angle estimation performance than estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Furthermore, the proposed algorithm has close angle estimation performance to trilinear decomposition. The proposed algorithm can be regarded as a combination of trilinear model and compressive sensing theory, and it brings much lower computational complexity and much smaller demand for storage capacity. Numerical simulations present the effectiveness of our approach.

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.

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 Three-Dimensional Coprime Array for Massive MIMO: Array Configuration Design and 2D DOA Estimation

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Pan Gong ◽  
Tanveer Ahmed ◽  
Jianfeng Li
Keyword(s):  
Computational Complexity ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Three Dimensional ◽  
Doa Estimation ◽  
Configuration Design ◽  
Computationally Efficient ◽  
Estimation Performance ◽  
2D Doa Estimation ◽  
Array Aperture

In massive multiple-input multiple-output (MIMO) systems, it is critical to obtain the accurate direction of arrival (DOA) estimation. Conventional three-dimensional array mainly focuses on the uniform array. Due to the dense arrangement of the sensors, the array aperture is limited and severe mutual coupling effects arise. In this paper, a coprime cubic array (CCA) configuration design is presented, which is composed of two uniform cubic subarrays and can extend the interelement spacing with a selection of three pairs of coprime integers. Compared with uniform cubic array (UCA), CCA achieves the larger array aperture and less MC effects. And the analytical expression of Cramer–Rao Bound (CRB) for CCA is derived which verifies that the proposed CCA geometry outperforms the conventional UCA in two-dimensional (2D) DOA estimation performance in massive MIMO systems. Meanwhile, we propose a computationally efficient 2D DOA estimation algorithm with high accuracy for CCA. Specifically, we utilize array mapping to extract two uniform arrays from the nonuniform array by exploiting the relation derived from the signal subspace and the two directional matrices. Then, we operate a reduced dimension process on the uniform arrays and convert the 2D spectrum peak searching (SPS) problem into one-dimensional (1D) one, which significantly reduces the computational complexity. In addition, we employ the polynomial root finding technique with a lower complexity instead of 1D SPS to further relieve the computational complexity. Simultaneously, with coprime property, the phase ambiguity problem is solved, which results from the large interelement spacing. Numerical simulation results demonstrate that the proposed algorithm is very computationally efficient without degradation of DOA estimation performance.

scholarly journals Computationally Efficient Ambiguity-Free Two-Dimensional DOA Estimation Method for Coprime Planar Array: RD-Root-MUSIC Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Luo Chen ◽  
Changbo Ye ◽  
Baobao Li
Keyword(s):  
Degrees Of Freedom ◽  
Estimation Method ◽  
Doa Estimation ◽  
Estimation Accuracy ◽  
Two Dimensional ◽  
Computationally Efficient ◽  
Music Algorithm ◽  
Estimation Algorithms ◽  
Planar Array ◽  
2D Doa Estimation

While the two-dimensional (2D) spectral peak search suffers from expensive computational burden in direction of arrival (DOA) estimation, we propose a reduced-dimensional root-MUSIC (RD-Root-MUSIC) algorithm for 2D DOA estimation with coprime planar array (CPA), which is computationally efficient and ambiguity-free. Different from the conventional 2D DOA estimation algorithms based on subarray decomposition, we exploit the received data of the two subarrays jointly by mapping CPA to the full array of the CPA (FCPA), which contributes to the enhanced degrees of freedom (DOFs) and improved estimation performance. In addition, due to the ambiguity-free characteristic of the FCPA, the extra ambiguity elimination operation can be avoided. Furthermore, we convert the 2D spectral search process into 1D polynomial rooting via reduced-dimension transformation, which substantially reduces the computational complexity while preserving the estimation accuracy. Finally, numerical simulations demonstrate the superiority of the proposed algorithm.

Interference Localization via Direction of Arrival Estimation for Satellite with Planar Array

2014 ◽  
Vol 1049-1050 ◽  
pp. 1788-1791
Author(s):  
Xiao Feng Qiu ◽  
Xiao Fei Zhang
Keyword(s):  
Computational Cost ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Music Algorithm ◽  
Angle Estimation ◽  
Multiple Signal Classification ◽  
Estimation Performance ◽  
Planar Array ◽  
Esprit Algorithm

This paper presents the model of satellite planar array, and interference localization via direction of arrival (DOA) estimation. We derive a dimension reduction DOA estimaton algorithm therein. The proposed algorithm, which only requires a one-dimensional local searching, can avoid the high computational cost within two-dimensional multiple signal classification (2D-MUSIC) algorithm. We illustrate that the proposed algorithm has better angle estimation performance than estimation method of signal parameters via rotational invariance technique (ESPRIT) algorithm, and has very close angle estimation performance to 2D-MUSIC algorithm. Furthermore, our algorithm requires no extra pairing. Simulation results present the usefulness of our algorithm.

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.

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.

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