A modular multichannel multiexperiment fast transversal filter RLS algorithm

A fast transversal filter (FTF) algorithm is proposed for solving multichannel multiexperiment recursive least-squares (RLS) problems that exhibit a shift structure between consecutive regression vectors. The sequential processing of the different channels and experiments decomposes the multichannel multiexperiment algorithm into a set of intertwined single-channel single-experiment algorithms, resulting in a modular algorithm structure. The sequential processing strategy corresponds to a triangular factorization of error covariance matrices and numerical benefits accrue from this approach. Furthermore, stabilization techniques for proper control of the propagation of numerical errors in the update recursions of FTF algorithms can also be straightforwardly incorporated. The algorithm is derived under the prewindowing assumption. In a companion paper the authors show how the proposed algorithm provides a framework of the covariance windowing cases (see ibid., vol.28, no.1, p.47-61, 1992).