Title :
Statistically robust signal subspace identification
Author :
Ammann, Larry P.
Author_Institution :
Center for Eng. Math., Texas Univ., Dallas, Richardson, TX, USA
Abstract :
The problem of signal subspace identification in the presence of transient, high-power noise or non-Gaussian noise is considered. To overcome such problems, an algorithm that results in a statistically robust singular value decomposition is proposed. This algorithm is derived from the connection between least-squares regression and the singular value decomposition. The robust singular value decomposition is then applied to the problem of estimation of the eigenstructure of a covariance matrix from raw data. The result of a Monte Carlo simulation study are presented to illustrate the effectiveness of the approach
Keywords :
Monte Carlo methods; identification; random noise; signal detection; Monte Carlo simulation study; eigenstructure; high-power noise; least-squares regression; nonGaussian noise; signal subspace identification; statistically robust singular value decomposition; Covariance matrix; Eigenvalues and eigenfunctions; Iterative algorithms; Least squares methods; Mathematics; Noise robustness; Sensor arrays; Signal processing; Signal processing algorithms; Singular value decomposition;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on
Conference_Location :
Albuquerque, NM
DOI :
10.1109/ICASSP.1990.116185