A novel subspace tracking using correlation-based projection approximation

In this paper, we propose a novel subspace estimation technique, which is c alled correlation-based projection approximation subspace tracking (COPAST) . The COPAST utilizes the projection approximation approach onto the correl ation matrix to develop the subspace tracking algorithm. With the projectio n approximation, the RLS-based COPAST and the sequential-based COPAST algor ithms are presented. The :RLS-based COPAST algorithm has the better perform ance but the higher computational complexity than the recently developed PA ST method. On the other hand, the sequential-based COPAST has reduced the c omputational complexity to nearly that of the PAST. Besides, the sequential -based COPAST has faster initial convergence speed than the PAST, while bot h nearly converge to the same value. (C) 2000 Elsevier Science B.V. All rig hts reserved.