DOA estimation of multiple sources using arbitrary microphone array configuration in the presence of spatial aliasing

Author(s):  
Masashi Sekikawa ◽  
Nozomu Hamada
2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Nizar Tayem

We addressed the problem of two-dimensional (2D) direction-of-arrival (DOA) elevation and azimuth angles estimation for multiple uncorrelated signals using L-shaped antenna array configuration. The key points of the proposed method are the following: (1) it obtains azimuth and elevation angles through construction of three cross-correlation matrices from the collected data of the received signals; this implies that the noise reduces significantly in the reconstructed data matrices; (2) it derives a parallel factor analysis (PARAFAC) model and applies trilinear least squares method to avoid pair matching problem between 2D DOA azimuth and elevation angles for multiple sources; (3) it does not require spectral peak searching; and (4) it has better 2D DOA estimation compared with signal parameters via rotational invariance technique and fourth-order signal parameters via rotational invariance technique. Simulation results demonstrate the estimation accuracy and the effectiveness of the proposed method.


Author(s):  
Weilin Tu ◽  
Dazhuan Xu ◽  
Ying Zhou ◽  
Chao Shi

Abstract Direction of arrival (DOA) estimation has been discussed extensively in the array signal processing field. In this paper, the authors focus on the multi-source DOA information which is defined as the mutual information between the DOA and the received signal contaminated by complex additive white Gaussian noise. A theoretical expression of DOA information with multiple sources is derived for the uniform linear array. At high SNRs and under the sparse-source assumption obtained is the upper bound of DOA information contained in K sparse sources which can be regarded as the sum of all single-source information minus the uncertainty of sources’ order logK!. Moreover, because of the uncertainty of multi-sources’ order, the posteriori probability distribution of DOA no longer obeys single peak Gaussian distribution so that the mean square error is unsuitable in evaluating the performance of multi-dimensional parameter estimation. Consequently, entropy error (EE) is used as a new performance evaluation metric, whose relationship with DOA information is given.


Author(s):  
Saeed M. Alamoudi ◽  
Mohammed A. Aldhaheri ◽  
Saleh A. Alawsh ◽  
Ali H. Muqaibel

Author(s):  
Israel Mendoza Velazquez ◽  
Yi Ren ◽  
Yoichi Haneda ◽  
Hector Manuel Perez Meana

2011 ◽  
Vol 130 (4) ◽  
pp. 2451-2451 ◽  
Author(s):  
Kai-Chung Tam ◽  
Siu-Kit Lau ◽  
Shiu-Keung Tang

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