Title :
Asymptotic distribution of recursive subspace estimators
Author :
Yang, Ban ; Gersemsky, Frank
Author_Institution :
Dept. of Electr. Eng., Ruhr-Univ., Bochum, Germany
Abstract :
We derive the asymptotic distribution of recursive subspace estimators. In particular, we study the PAST algorithm for tracking the signal subspace and the Oja (1982) rule for updating the eigenvector corresponding to the largest eigenvalue. Both the decreasing gain and the constant gain case are considered. It turns out that their asymptotic distributions differ from that of the batch eigenvalue decomposition. The asymptotic rate of convergence is also addressed
Keywords :
convergence of numerical methods; eigenvalues and eigenfunctions; recursive estimation; signal processing; statistical analysis; tracking; Oja rule; PAST algorithm; asymptotic convergence rate; asymptotic distribution; asymptotic statistics; batch eigenvalue decomposition; constant gain; decreasing gain; eigenvector updating; recursive subspace estimators; signal processing; signal subspace tracking; Convergence; Covariance matrix; Eigenvalues and eigenfunctions; Performance analysis; Recursive estimation; Signal processing; Signal processing algorithms; Singular value decomposition; Statistical distributions; Statistics;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
0-7803-3192-3
DOI :
10.1109/ICASSP.1996.550149
