Skip to main content
SpringerLink
Log in

Adaptive Signal Processing

  • Book
  • © 1991

Overview

Editors:
  1. L. D. Davisson

    1. University of Maryland, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. G. Longo

    1. University of Trieste, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

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

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (4 chapters)

  1. Front Matter

    Pages i-v
    Download chapter PDF

  2. Adaptive Linear Prediction and Process Order Identification

    • T. C. Butash, L. D. Davisson
    Pages 1-67

  3. Linear Prediction Techniques in Speech Coding

    • A. Gersho
    Pages 69-95

  4. Adaptive Processing in Sensor Arrays

    • M. Wax
    Pages 97-171

  5. Nonstandard Methods in Prediction

    • H. V. Poor
    Pages 173-203
Back to top

Keywords

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.

Editors and Affiliations

  • University of Maryland, USA

    L. D. Davisson

  • University of Trieste, USA

    G. Longo

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation