Linearly constrained block adaptive filtering algorithm with optimum convergence factors

摘要

A new linearly constrained adaptive filtering algorithm, the linearly constrained optimum block adaptive (LCOBA) algorithm, is presented. The LCOBA algorithm processes data in blocks and uses variable convergence factors which are optimised in a least square sense. It is superior to Frost's linearly constrained least mean squares algorithm at achieving the conflicting goals of fast convergence with little steady-state error. In addition, its computational requirements generally tend to be smaller than that of the Frost algorithm, as the block length is increased.