Two Improved Algorithms for Adaptive Subspace Filtering

Abstract

In this paper, two improved algorithms with less computational complexities for adaptive subspace filtering are proposed. First, for signal subspace filtering, the basic projection approximation subspace tracking (PAST) algorithm by Yang is modified to update only the diagonal elements of the covariance matrix. Second, for noise subspace filtering, especially the extraction of the eigenvector associated with the minimum eigenvalue, a modified PAST algorithm by putting the minus sign to the adaptive gain with the normalization is proposed. Using the ODE approach, the convergent points of both algorithms are shown to be the desired ones. Some simulation results show that both algorithms have better numerical and convergent properties than the existing algorithms.