scholarly journals 2D-DOA Estimation of Noncircular Signals for Uniform Rectangular Array via Noncircular-ESPRIT

2016 ◽  
Author(s):  
Weihua Lv ◽  
Wang Zheng ◽  
Xiaofei Zhang ◽  
Licen Zhang
Keyword(s):  
Doa Estimation ◽  
Rectangular Array ◽  
2D Doa Estimation ◽  
Noncircular Signals ◽  
Uniform Rectangular Array

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

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.

scholarly journals Utilizing Principal Singular Vectors for 2D DOA Estimation in Single Snapshot Case with Uniform Rectangular Array

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Yuntao Wu ◽  
Xiaobing Pei ◽  
Hing Cheung So
Keyword(s):  
Iterative Procedure ◽  
Linear Prediction ◽  
Weighted Least Squares ◽  
Doa Estimation ◽  
Data Matrix ◽  
Rectangular Array ◽  
Singular Vectors ◽  
2D Doa Estimation ◽  
Single Snapshot ◽  
Uniform Rectangular Array

The problem of azimuth and elevation directions of arrival (DOAs) estimation using a uniform rectangular array (URA) in single snapshot case is addressed in this paper. Using the principal singular vectors of the observed data matrix, an iterative procedure based on the linear prediction property, and weighted least squares is proposed for finding the DOAs with lower computational complexity. Furthermore, the azimuth and elevation parameters are automatically paired. Computer simulations are included to demonstrate 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 Computationally Efficient 2D DOA Estimation with Uniform Rectangular Array in Low-Grazing Angle

Sensors ◽  
2017 ◽  
Vol 17 (3) ◽  
pp. 470 ◽  
Author(s):  
Junpeng Shi ◽  
Guoping Hu ◽  
Xiaofei Zhang ◽  
Fenggang Sun ◽  
Yu Xiao
Keyword(s):  
Grazing Angle ◽  
Doa Estimation ◽  
Computationally Efficient ◽  
Rectangular Array ◽  
2D Doa Estimation ◽  
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.

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