Chandrasekhar-type filter for a wide-sense stationary signal from uncertain observations using covariance information

摘要

In this paper, the least mean-squared error linear filtering problem of a wide-sense stationary signal, from uncertain observations perturbed by a white noise, is approached by means of a Chandrasekhar-type recursive algorithm. In comparison with a Riccati-type algorithm, the proposed algorithm is more advantageous from a computational point of view, since it reduces the number of difference equations contained in it and, consequently, the computation time. The algorithm is derived by using covariance information, without requiring that the state-space model of the signal is completely known.