Overview
- 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)
Keywords
- Acoustic Signal Processing
- Array Signal Processing
- Arrays for Detection
- Arrays for Localization
- Autogregressive Moving Average
- Bearing Estimation
- CRLB Bound
- Estimator Quality
- Kalman Filter Processing for Arrays
- Model-Based Signal Processing
- Model-based Array Processing
- Model-based Array Processors
- Passive Acoustic Arrays
- Passive Underwater Acoustic Arrays
- Passive Underwater Acoustic Arrays
- Underwater Acoustic Arrays
- fluid- and aerodynamics
- remote sensing/photogrammetry
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
Bibliographic Information
Book Title: Model-Based Processing for Underwater Acoustic Arrays
Authors: Edmund J. Sullivan
Series Title: SpringerBriefs in Physics
DOI: https://doi.org/10.1007/978-3-319-17557-7
Publisher: Springer Cham
eBook Packages: Physics and Astronomy, Physics and Astronomy (R0)
Copyright Information: Edmund J. Sullivan 2015
Softcover ISBN: 978-3-319-17556-0Published: 09 June 2015
eBook ISBN: 978-3-319-17557-7Published: 14 May 2015
Series ISSN: 2191-5423
Series E-ISSN: 2191-5431
Edition Number: 1
Number of Pages: X, 113
Number of Illustrations: 11 b/w illustrations, 14 illustrations in colour
Topics: Fluid- and Aerodynamics, Signal, Image and Speech Processing, Oceanography, Engineering Acoustics, Geophysics and Environmental Physics, Remote Sensing/Photogrammetry