On structured total least squares for blind identification of multichannel FIR filters

Structured total least-squares (STLS) provides a nice framework for approximating a full-rank affmely-structured matrix with a rank-deficient matrix having the same affine structure. In this paper, we investigate the use of STLS method for blind identification of multiple FIR channels driven by an unknown deterministic input. First, we exploit the block - Hankel affine structure of the data matrix, which motivates the use of STLS-based methods. Then, we derive an iterative non-linear solution to the unknown channel parameters by using a generalized form of singular value decomposition. We carry out extensive numerical simulations to compare the performance of the proposed method against the well-known least-squares (LS) method, where the affine structure of the date matrix is overlooked. These results reveal that the STLS based method outperforms the LS method for ill-conditioned as well as well-conditioned channels over a wide range of SNR.