2D-DOA Estimation for Co-prime L-shaped Arrays with Propagator Method

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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Compressed Sensing PARALIND Decomposition-Based Coherent Angle Estimation for Uniform Rectangular Array

Wireless Communications and Mobile Computing ◽

10.1155/2019/3619818 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10

Author(s):

Wu Wei ◽

Xu Le ◽

Zhang Xiaofei ◽

Li Jianfeng

Keyword(s):

Compressed Sensing ◽

Data Storage ◽

Estimation Algorithm ◽

Doa Estimation ◽

Spatial Smoothing ◽

Rectangular Array ◽

Propagator Method ◽

Coherent Sources ◽

2D Doa Estimation ◽

Uniform Rectangular Array

In this paper, the topic of coherent two-dimensional direction of arrival (2D-DOA) estimation is investigated. Our study jointly utilizes the compressed sensing (CS) technique and the parallel profiles with linear dependencies (PARALIND) model and presents a 2D-DOA estimation algorithm for coherent sources with the uniform rectangular array. Compared to the traditional PARALIND decomposition, the proposed algorithm owns lower computational complexity and smaller data storage capacity due to the process of compression. Besides, the proposed algorithm can obtain autopaired azimuth angles and elevation angles and can achieve the same estimation performance as the traditional PARALIND, which outperforms some familiar algorithms presented for coherent sources such as the forward backward spatial smoothing-estimating signal parameters via rotational invariance techniques (FBSS-ESPRIT) and forward backward spatial smoothing-propagator method (FBSS-PM). Extensive simulations are provided to validate the effectiveness of the proposed CS-PARALIND algorithm.

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

International Journal of Antennas and Propagation ◽

10.1155/2015/485351 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 8

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.

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