Batch Covariance Relaxation (BCR) Adaptive Processing

S. M. Daniel

doi:10.1117/12.932526

30 July 1982 Batch Covariance Relaxation (BCR) Adaptive Processing

S. M. Daniel

Proceedings Volume 0298, Real-Time Signal Processing IV; (1982) https://doi.org/10.1117/12.932526
Event: 25th Annual Technical Symposium, 1981, San Diego, United States

Abstract

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.

Citation Download Citation

S. M. Daniel "Batch Covariance Relaxation (BCR) Adaptive Processing", Proc. SPIE 0298, Real-Time Signal Processing IV, (30 July 1982); https://doi.org/10.1117/12.932526

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