Two dimensional DOA estimation using uniform rectangular array with mutual coupling

2014 ◽  
Author(s):  
Mustafa Caliskan ◽  
T. Engin Tuncer
Keyword(s):  
Mutual Coupling ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Rectangular Array ◽  
Uniform Rectangular Array

Two-Dimensional Direction Finding Estimation for Uniform Rectangular Array with Unknown Mutual Coupling via Real-Valued PARAFAC Decomposition

2019 ◽  
Vol 28 (03) ◽  
pp. 1950049
Author(s):  
Lingyun Xu ◽  
Fangqing Wen
Keyword(s):  
Mutual Coupling ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Music Algorithm ◽  
Rectangular Array ◽  
Lower Complexity ◽  
2D Doa Estimation ◽  
Esprit Algorithm ◽  
Parafac Decomposition ◽  
Uniform Rectangular Array

Two-dimensional direction-of-arrival (2D-DOA) estimation for uniform rectangular array (URA) is a canonical problem with numerous applications, e.g., wireless communications, sonar and radar systems. The conventional 2D-DOA estimators usually are derived with the assumption of ideal arrays. However, in practice, the arrays may not be well calibrated and suffer from unknown mutual coupling. Using the conventional estimators may lead to low accuracy estimation and high computational complexity in the condition of large number of array elements. In this paper, a novel real-valued parallel factor (PARAFAC) decomposition algorithm is proposed to tackle this problem. The proposed algorithm has better angle estimation performance than the multiple signal classification (MUSIC) algorithm, estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm and conventional PARAFAC algorithm. But it has lower complexity than MUSIC algorithm. Moreover, the proposed algorithm can obtain automatically paired 2D-DOA estimation, and it is suitable to coherent or closely spaced signals and can eliminate the mutual coupling. Simulation results verify the effectiveness of the proposed algorithm.

scholarly journals Two-Dimensional DOA Estimation for Uniform Rectangular Array Using Reduced-Dimension Propagator Method

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Ming Zhou ◽  
Xiaofei Zhang ◽  
Xiaofeng Qiu ◽  
Chenghua Wang
Keyword(s):  
Estimation Error ◽  
Doa Estimation ◽  
Eigenvalue Decomposition ◽  
Two Dimensional ◽  
Rectangular Array ◽  
Angle Estimation ◽  
Propagator Method ◽  
Reduced Dimension ◽  
2D Doa Estimation ◽  
Uniform Rectangular Array

A novel algorithm is proposed for two-dimensional direction of arrival (2D-DOA) estimation with uniform rectangular array using reduced-dimension propagator method (RD-PM). The proposed algorithm requires no eigenvalue decomposition of the covariance matrix of the receive data and simplifies two-dimensional global searching in two-dimensional PM (2D-PM) to one-dimensional local searching. The complexity of the proposed algorithm is much lower than that of 2D-PM. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm and conventional PM algorithms, also very close to 2D-PM. The angle estimation error and Cramér-Rao bound (CRB) are derived in this paper. Furthermore, the proposed algorithm can achieve automatically paired 2D-DOA estimation. The simulation results verify the effectiveness of the algorithm.

scholarly journals DOA Estimation of Two-Dimensional Coherently Distributed Sources Based on Spatial Smoothing of Uniform Rectangular Arrays

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Tao Wu ◽  
Yiwen Li ◽  
Xiaofeng Zhang ◽  
Yijie Huang ◽  
Qingyue Gu ◽  
...  
Keyword(s):  
Doa Estimation ◽  
Covariance Matrices ◽  
Two Dimensional ◽  
Spatial Smoothing ◽  
Rectangular Array ◽  
Signal Distribution ◽  
Sample Covariance Matrices ◽  
Sample Covariance ◽  
Propagator Method ◽  
Uniform Rectangular Array

Aiming at the direction-of-arrival (DOA) estimation of two-dimensional (2D) coherently distributed (CD) sources which are coherent with each other, we explore the propagator method based on spatial smoothing of a uniform rectangular array (URA). The rotational invariance relationships with respect to the nominal azimuth and nominal elevation are obtained under the small angular spreads assumption. A propagator operator is constructed through spatial smoothing of sample covariance matrices firstly. Then, combination of propagator and identical matrix is divided according to rotational operators, and the nominal angles can be obtained through eigendecomposition lastly. Realizing angle matching automatically, the proposed method can estimate multiple DOAs of 2D coherent CD sources without spectral peak searching and prior knowledge of deterministic angular signal distribution function. Simulations are conducted to verify the effectiveness of the proposed method.

scholarly journals Thermos Array: Two-Dimensional Sparse Array with Reduced Mutual Coupling

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Lei Sun ◽  
Minglei Yang ◽  
Baixiao Chen
Keyword(s):  
Closed Form ◽  
Degrees Of Freedom ◽  
Mutual Coupling ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Spatial Smoothing ◽  
Planar Arrays ◽  
Half Wavelength ◽  
Planar Array ◽  
Small Spacing

Sparse planar arrays, such as the billboard array, the open box array, and the two-dimensional nested array, have drawn lots of interest owing to their ability of two-dimensional angle estimation. Unfortunately, these arrays often suffer from mutual-coupling problems due to the large number of sensor pairs with small spacing d (usually equal to a half wavelength), which will degrade the performance of direction of arrival (DOA) estimation. Recently, the two-dimensional half-open box array and the hourglass array are proposed to reduce the mutual coupling. But both of them still have many sensor pairs with small spacing d, which implies that the reduction of mutual coupling is still limited. In this paper, we propose a new sparse planar array which has fewer number of sensor pairs with small spacing d. It is named as the thermos array because its shape seems like a thermos. Although the resulting difference coarray (DCA) of the thermos array is not hole-free, a large filled rectangular part in the DCA can be facilitated to perform spatial-smoothing-based DOA estimation. Moreover, it enjoys closed-form expressions for the sensor locations and the number of available degrees of freedom. Simulations show that the thermos array can achieve better DOA estimation performance than the hourglass array in the presence of mutual coupling, which indicates that our thermos array is more robust to the mutual-coupling array.

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 DOA Tracking of Two-Dimensional Coherent Distribution Source Based on Fast Approximated Power Iteration

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Tao Wu ◽  
Pengtao Zhang ◽  
Yiwen Li ◽  
Yangjun Gao ◽  
Chaoqi Fu ◽  
...  
Keyword(s):  
Estimation Method ◽  
Doa Estimation ◽  
Distribution Functions ◽  
Tracking Algorithm ◽  
Signal Subspace ◽  
Two Dimensional ◽  
Model Errors ◽  
Rectangular Array ◽  
Linear Arrays ◽  
Power Iteration

Aiming at two-dimensional (2D) coherent distributed (CD) sources, this paper has proposed a direction of arrival (DOA) tracking algorithm based on signal subspace updating under the uniform rectangular array (URA). First, based on the hypothesis of small angular spreads of distributed sources, the rotating invariant relations of the signal subspace of the receive vector of URA are derived. An ESPRIT-like method is constructed for DOA estimation using two adjacent parallel linear arrays of URA. Through the synthesis of estimation by multiple groups of parallel linear arrays within URA arrays, the DOA estimation method for 2D CD sources based on URA is obtained. Then, fast approximated power iteration (FAPI) subspace tracking algorithm is used to update the signal subspace. In this way, DOA tracking of 2D CD sources can be realized by DOA estimation through signal subspace updating. This algorithm has a low computational complexity and good real-time tracking performance. In addition, the algorithm can track multiple CD sources without knowing the angular signal distribution functions, which is robust to model errors.

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