Batch Covariance Relaxation (BCR) Adaptive Processing
Sarpong Daniel · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1982
A BCR adaptive process [1], based on the Conjugate Gradients (CG) method [2], is offered as an alternative to a Sample Matrix Inversion (SMI) [3] approach to solving minimum-mean-square (MMS) problems. In contrast to SMI, BCR does not require that a matrix inverse exist. This point is demonstrated via computer simulation for the case of an adaptive array processing example. Furthermore, BCR lends itself to a simple and efficient fixed-point architecture capable of a numerical accuracy commensurate to sample word lengths, a fact substantiated via a precise computer emulation of the BCR implementation.