Localización Espacial de Fuentes en Comunicaciones Móviles empleando Actualización Sucesiva de Subespacio (DOA Estimation in Mobile Communications System using Subspace Tracking Methods)

Fabián Mendoza - Montoya (jfmendoz@cicese.mx)1, David H. Covarrubias-Rosales (dacoro@cicese.mx)1, Claudio A. Lopéz-Miranda (claudio@gauss.mat.uson.mx)2


1CICESE Centro de Investigación
2Universidad de Sonora

This paper appears in: Revista IEEE América Latina

Publication Date: June 2008
Volume: 6,   Issue: 2 
ISSN: 1548-0992


Abstract:
The radio environments in mobile communications are complicated and time-varing in general; therefore, we need high resolution DoA (direction of arrival) estimation methods that can follow quickly the change of radio environments. High resolution DoA estimation methods have been proposed which are based on the eigen decomposition of the correlation (covariance) matrix of an array input. MUSIC (Multiple Signal Classification) is one of a typical of such methods. However, these methods must normally repeat high-load computation involving the eigen decomposition of a correlation matrix every time a snapshot is taken. Therefore, it takes a very long time to obtain the estimated DoA when the number of array elements is too large. In addition, it is quite inefficient in the case that the DoA estimation is carried out continuously. To solve the above problems, Bi-SVD (Bi-Iteration Singular Value Decomposition) and PAST (Projection Approximation Subspace Tracking) have been proposed and investigated, which are typical methods of successively updating (tracking) eigenvectors in the signal subspace of correlation matrix. The radio environments in mobile communications are complicated and time-varing in general; therefore, we need high resolution DoA (direction of arrival) estimation methods that can follow quickly the change of radio environments. High resolution DoA estimation methods have been proposed which are based on the eigen decomposition of the correlation (covariance) matrix of an array input. MUSIC (Multiple Signal Classification) is one of a typical of such methods. However, these methods must normally repeat high-load computation involving the eigen decomposition of a correlation matrix every time a snapshot is taken. Therefore, it takes a very long time to obtain the estimated DoA when the number of array elements is too large. In addition, it is quite inefficient in the case that the DoA estimation is carried out continuously. To solve the above problems, Bi-SVD (Bi-Iteration Singular Value Decomposition) and PAST (Projection Approximation Subspace Tracking) have been proposed and investigated, which are typical methods of successively updating (tracking) eigenvectors in the signal subspace of correlation matrix. In this work, we explain a scheme to incorporate those two subspace tracking methods into MUSIC and shows a comparison of DoA successive estimation performance.

Index Terms:
DOA estimation, subspace tracking methods, mobile communications, MUSIC, PAST   


Documents that cite this document
This function is not implemented yet.


[PDF Full-Text (218)]