Adaptive Signal Processing [electronic resource] /

The four chapters of this volume, written by prominent workers in the field of adaptive processing and linear prediction, address a variety of problems, ranging from adaptive source coding to autoregressive spectral estimation. The first chapter, by T.C. Butash and L.D. Davisson, formulates the performance of an adaptive linear predictor in a series of theorems, with and without the Gaussian assumption, under the hypothesis that its coefficients are derived from either the (single) observation sequence to be predicted (dependent case) or a second, statistically independent realisation (independent case). The contribution by H.V. Poor reviews three recently developed general methodologies for designing signal predictors under nonclassical operating conditions, namely the robust predictor, the high-speed Levinson modeling, and the approximate conditional mean nonlinear predictor. W. Wax presents the key concepts and techniques for detecting, localizing and beamforming multiple narrowband sources by passive sensor arrays. Special coding algorithms and techniques based on the use of linear prediction now permit high-quality voice reproduction at remorably low bit rates. The paper by A. Gersho reviews some of the main ideas underlying the algorithms of major interest today.

Bibliographic Details

Main Authors:	Davisson, L. D. editor., Longo, G. editor., SpringerLink (Online service)
Format:	Texto biblioteca
Language:	eng
Published:	Vienna : Springer Vienna : Imprint: Springer, 1991
Subjects:	Computer science., Coding theory., Probabilities., Electrical engineering., Computer Science., Coding and Information Theory., Probability Theory and Stochastic Processes., Communications Engineering, Networks.,
Online Access:	http://dx.doi.org/10.1007/978-3-7091-2840-4
Tags:	Add Tag No Tags, Be the first to tag this record!