Rotation-based RLS algorithms: unified derivations, numerical properties, and parallel implementations

摘要

This work presents a unified derivation of four rotation-based recursive least squares (RLS) algorithms. They solve the adaptive least squares problems of the linear combiner, the linear combiner without a desired signal, the single channel, and the multichannel linear prediction and transversal filtering. Compared to other approaches, the authors' derivation is simpler and unified, and may be useful to readers for better understanding the algorithms and their relationships. Moreover, it enables improvements of some algorithms in the literature in both the computational and the numerical issues. All algorithms derived in this work are based on Givens rotations. They offer superior numerical properties as shown by computer simulations. They are computationally efficient and highly concurrent. Aspects of parallel implementation and parameter identification are discussed.