A novel subspace tracking using correlation-based projection approximation

摘要

In this paper, we propose a novel subspace estimation technique, which is called correlation-based projection approximation subspace tracking (COPAST). The COPAST utilizes the projection approximation approach onto the correlation matrix to develop the subspace tracking algorithm. With the projection approximation, the RLS-based COPAST and the sequential-based COPAST algorithms are presented. The RLS-based COPAST algorithm has the better performance but the higher computational complexity than the recently developed PAST method. On the other hand, the sequential-based COPAST has reduced the computational complexity to nearly that of the PAST. Besides, the sequential-based COPAST has faster initial convergence speed than the PAST, while both nearly converge to the same value.