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  • © 2015

Model-Based Processing for Underwater Acoustic Arrays

  • Represents the first unified approach to model-based array processing
  • Presents experimentally verified examples
  • Sufficiently self-contained to allow use by practicing engineers and researchers
  • Includes an extensive reference list for further study

Part of the book series: SpringerBriefs in Physics (SpringerBriefs in Physics)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-x
  2. Introduction

    • Edmund J. Sullivan
    Pages 1-8
  3. The Acoustic Array

    • Edmund J. Sullivan
    Pages 9-25
  4. Statistical Signal Processing Overview

    • Edmund J. Sullivan
    Pages 27-49
  5. From Bayes to Kalman

    • Edmund J. Sullivan
    Pages 51-73
  6. Applications

    • Edmund J. Sullivan
    Pages 75-104
  7. Filter Tuning and Solution Testing

    • Edmund J. Sullivan
    Pages 105-109
  8. Back Matter

    Pages 111-113

About this book

This monograph presents a unified approach to model-based processing for underwater acoustic arrays. The use of physical models in passive array processing is not a new idea, but it has been used on a case-by-case basis, and as such, lacks any unifying structure. This work views all such processing methods as estimation procedures, which then can be unified by treating them all as a form of joint estimation based on a Kalman-type recursive processor, which can be recursive either in space or time, depending on the application. This is done for three reasons. First, the Kalman filter provides a natural framework for the inclusion of physical models in a processing scheme. Second, it allows poorly known model parameters to be jointly estimated along with the quantities of interest. This is important, since in certain areas of array processing already in use, such as those based on matched-field processing, the so-called mismatch problem either degrades performance or, indeed, prevents any solution at all. Thirdly, such a unification provides a formal means of quantifying the performance improvement. The term model-based will be strictly defined as the use of physics-based models as a means of introducing a priori information. This leads naturally to viewing the method as a Bayesian processor. Short expositions of estimation theory and acoustic array theory are presented, followed by a presentation of the Kalman filter in its recursive estimator form. Examples of applications to localization, bearing estimation, range estimation and model parameter estimation are provided along with experimental results verifying the method. The book is sufficiently self-contained to serve as a guide for the application of model-based array processing for the practicing engineer.

Authors and Affiliations

  • Portsmouth, USA

    Edmund J. Sullivan

Bibliographic Information

Buy it now

Buying options

eBook USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 64.99
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