A constrained optimization approach for an adaptive generalized subspace tracking algorithm

In this paper, we present a new algorithm for tracking the generalized signal subspace recursively. It is based on an interpretation of the generalized signal subspace as the solution of a constrained minimization task. This algorithm, referred to as the CGST algorithm, guarantees the Cx-orthonormality of the estimated generalized signal subspace basis at each iteration which Cx denotes the correlation matrix of the sequence x(t). Thus, the proposed algorithm avoids Cxorthonormalization process after each update for postprocessing algorithms which need Cx-orthonormal basis for the generalized signal subspace. An efficient implementation of the proposed algorithm enhances applicability of it in real time applications. Various simulation results show outstanding performance of the proposed algorithm.