Modular and numerically stable fast transversal filters for multichannel and multiexperiment RLS

The authors present scalar implementations of multichannel and multiexperiment fast recursive least squares algorithms in transversal filter form, known as fast transversal filter (FTF) algorithms. By processing the different channels and/or experiments one at a time, the multichannel and/or multiexperiment algorithm decomposes into a set of intertwined single-channel single-experiment algorithms. For multichannel algorithms, the general case of possibly different filter orders in different channels is handled. Geometrically, this modular decomposition approach corresponds to a Gram-Schmidt orthogonalization of multiple error vectors. Algebraically, this technique corresponds to matrix triangularization of error covariance matrices and converts matrix operations into a regular set of scalar operations. Modular algorithm structures that are amenable to VLSI implementation on arrays of parallel processors naturally follow from the present approach. Numerically, the resulting algorithm benefits from the advantages of triangularization techniques in block processing