Abstract
In this paper, we present a recursive algorithm for a subspace identification. Using the results of the subspace extraction via Schur complement (SES) approach, we derive a recursive formula of an error covariance matrix in subspace methods. This recursive procedure requires the eigenvalue decomposition (EVD) of the covariance matrix at each step. Therefore we introduce the rank-k modification algorithm with the spectrum slicing theory and achive to update the EVD recursively. The algorithm improves a computational complexity from O(N3) to O(N2k) class where N x N is the size of the covariance matrix.
