Two Dimensional Direction of Arrival Estimation using Single Snapshot of Nonuniformly Spaced Planar Array

2006 ◽  
Author(s):  
Hassan M. Elkamchouchi ◽  
Darwish Abdel-Aziz ◽  
Mohammad M. M. Omar
Keyword(s):  
Direction Of Arrival ◽  
Direction Of Arrival Estimation ◽  
Two Dimensional ◽  
Planar Array ◽  
Single Snapshot

Direction-of-arrival estimation of multiple speakers using a planar array

2014 ◽  
Author(s):  
Dongwen Ying ◽  
Ruohua Zhou ◽  
Junfeng Li ◽  
Jielin Pan ◽  
Yonghong Yan
Keyword(s):  
Direction Of Arrival ◽  
Direction Of Arrival Estimation ◽  
Planar Array

scholarly journals Two-Dimensional Direction-of-Arrival Fast Estimation of Multiple Signals with Matrix Completion Theory in Coprime Planar Array

Sensors ◽  
2018 ◽  
Vol 18 (6) ◽  
pp. 1741 ◽  
Author(s):  
Haiyun Xu ◽  
Yankui Zhang ◽  
Bin Ba ◽  
Daming Wang ◽  
Xiangzhi Li
Keyword(s):  
Matrix Completion ◽  
Direction Of Arrival ◽  
Two Dimensional ◽  
Multiple Signals ◽  
Fast Estimation ◽  
Planar Array

scholarly journals Two-Dimensional Direction-of-Arrival Estimation of Noncircular Signals in Coprime Planar Array with High Degree of Freedom

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Haiyun Xu ◽  
Daming Wang ◽  
Size Lin ◽  
Bin Ba ◽  
Yankui Zhang
Keyword(s):  
Covariance Matrix ◽  
Direction Of Arrival ◽  
Low Complexity ◽  
Degree Of Freedom ◽  
Spectral Peak ◽  
Two Dimensional ◽  
Rectangular Array ◽  
Planar Array ◽  
Noncircular Signals ◽  
Peak Search

In estimating the two-dimensional (2D) direction-of-arrival (DOA) using a coprime planar array, there are problems of the limited degree of freedom (DOF) and high complexity caused by the spectral peak search. We utilize the time-domain characteristics of signals and present a high DOF algorithm with low complexity based on the noncircular signals. The paper first analyzes the covariance matrix and ellipse covariance matrix of the received signals, vectorizes these matrices, and then constructs the received data of a virtual uniform rectangular array (URA). 2D spatial smoothing processing is applied to calculate the covariance of the virtual URA. Finally, the paper presents an algorithm using 2D multiple signal classification and an improved algorithm using unitary estimating signal parameters via rotational invariance techniques, where the latter solves the closed-form solutions of DOAs replacing the spectral peak search to reduce the complexity. The simulation experiments demonstrate that the proposed algorithms obtain the high DOF and enable to estimate the underdetermined signals. Furthermore, both two proposed algorithms can acquire the high accuracy.

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