Title :
An Improved Subspace-Based Algorithm in the Small Sample Size Regime
Author :
Mestre, Xavier ; Rubio, Francisco
Author_Institution :
Centre Tecnologic de Telecomunicacions de Catalunya, Barcelona
Abstract :
A new method for subspace identification in array signal processing applications is proposed. The method is based on random matrix theory and provides consistent estimates even when the observation dimension increases without bound at the same rate as the number of observations. This guarantees a good behavior in finite sample size situations, where the number of sensors and the number of samples have the same order of magnitude. Consistency of the algorithm holds in situations where the signal and noise subspaces are asymptotically separable in the sense that, in the asymptotic sample eigenvalue distribution, signal and noise eigenvalues generate different spectral clusters
Keywords :
array signal processing; eigenvalues and eigenfunctions; matrix algebra; signal sampling; array signal processing; asymptotic sample eigenvalue distribution; improved subspace-based algorithm; noise subspaces; random matrix theory; small sample size regime; spectral clusters; subspace identification; Array signal processing; Clustering algorithms; Covariance matrix; Eigenvalues and eigenfunctions; Irrigation; Noise generators; Sensor arrays; Signal processing; Signal processing algorithms; Telecommunications;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on
Conference_Location :
Toulouse
Print_ISBN :
1-4244-0469-X
DOI :
10.1109/ICASSP.2006.1661158