Adaptive Signal Processing

  • L. D. Davisson
  • G. Longo

Part of the International Centre for Mechanical Sciences book series (CISM, volume 324)

Table of contents

  1. Front Matter
    Pages i-v
  2. T. C. Butash, L. D. Davisson
    Pages 1-67
  3. H. V. Poor
    Pages 173-203

About this book


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.


algorithms coding modeling Rang sensor signal processing

Editors and affiliations

  • L. D. Davisson
    • 1
  • G. Longo
    • 2
  1. 1.University of MarylandUSA
  2. 2.University of TriesteUSA

Bibliographic information

  • DOI
  • Copyright Information CISM Udine 1991
  • Publisher Name Springer, Vienna
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-211-82333-0
  • Online ISBN 978-3-7091-2840-4
  • Series Print ISSN 0254-1971
  • Series Online ISSN 2309-3706
  • Buy this book on publisher's site