Numerical performance of a tensor MUSIC algorithm based on HOSVD for a mixture of polarized sources

Author

Boizard, Maxime ; Ginolhac, Guillaume ; Pascal, F. ; Miron, Sebastian ; Forster, Philippe

Author_Institution

Lab. SATIE, ENS Cachan, Cachan, France

fYear

2013

fDate

9-13 Sept. 2013

Firstpage

1

Lastpage

5

Abstract

In this paper, we develop an improved tensor MUSIC algorithm adapted to multidimensional data by means of multilinear algebra tools. This approach allows to preserve the multidimensional structure as the signal and the noise subspaces are estimated from the Higher Order Singular Value Decomposition (HOSVD) of the covariance tensor. The proposed algorithm is applied to a polarized source model. By computing the Mean Squared Error (MSE) for different scenarios, the performance of this method is compared to the classical MUSIC algorithm as well as the vector MUSIC algorithm that includes the polarization information. The simulations show that our algorithm outperforms the vector algorithms.

Keywords

mean square error methods; numerical analysis; polarisation; signal classification; singular value decomposition; tensors; HOSVD; MSE; covariance tensor; higher order singular value decomposition; mean squared error; multidimensional data; multidimensional structure; multilinear algebra tools; noise subspaces; numerical performance; polarization information; polarized source model; polarized sources mixture; signal subspaces; tensor MUSIC algorithm; Direction-of-arrival estimation; Multiple signal classification; Sensors; Signal processing algorithms; Signal to noise ratio; Tensile stress; Vectors; DOA Polarimetric sources estimation; HOSVD; Tensor MUSIC;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 21st European

Conference_Location

Marrakech

Type

conf

Filename

6811540