DUAL-STATE SYSTOLIC ARCHITECTURES FOR UP DOWNDATING RLS ADAPTIVE FILTERING

Abstract

We propose a dual-state systolic structure to perform joint up/down-dating operations encountered in windowed recursive least-squares (RLS) estimation problems. It is based on successively performing Givens rotations for updating and hyperbolic rotations for downdating. Due to data independency, a series of Givens and hyperbolic rotations can be interleaved and parallel processing can be achieved by alternatively performing updating and downdating both in time and space. This flip-flop nature of up/down-dating characterizes the feature of the dual-state systolic triarray. Efficient implementation on the evaluation of optimal residuals is also considered. This systolic architecture is promising for the VLSI implementation of fixed size sliding-window recursive least-squares estimations.