Model-Based Processing for Underwater Acoustic Arrays

Nonfiction, Science & Nature, Science, Physics, Mechanics, Technology, Electronics
Cover of the book Model-Based Processing for Underwater Acoustic Arrays by Edmund J. Sullivan, Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Edmund J. Sullivan ISBN: 9783319175577
Publisher: Springer International Publishing Publication: May 14, 2015
Imprint: Springer Language: English
Author: Edmund J. Sullivan
ISBN: 9783319175577
Publisher: Springer International Publishing
Publication: May 14, 2015
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer International Publishing

Cover of the book Pursuit of the Universal by Edmund J. Sullivan
Cover of the book Issues in Science and Theology: What is Life? by Edmund J. Sullivan
Cover of the book Positive Systems by Edmund J. Sullivan
Cover of the book An Introduction to Relativistic Processes and the Standard Model of Electroweak Interactions by Edmund J. Sullivan
Cover of the book Public Opinion on Economic Globalization by Edmund J. Sullivan
Cover of the book Winning at Litigation through Decision Analysis by Edmund J. Sullivan
Cover of the book The Great Awakening and Southern Backcountry Revolutionaries by Edmund J. Sullivan
Cover of the book Grenfell Tower by Edmund J. Sullivan
Cover of the book The Neurobiological Basis of Memory by Edmund J. Sullivan
Cover of the book Hermeneutics of Human-Animal Relations in the Wake of Rewilding by Edmund J. Sullivan
Cover of the book Modern Concepts of Peripheral Nerve Repair by Edmund J. Sullivan
Cover of the book Species Concepts in Biology by Edmund J. Sullivan
Cover of the book Transfusion in the Intensive Care Unit by Edmund J. Sullivan
Cover of the book The Papanicolaou Society of Cytopathology System for Reporting Pancreaticobiliary Cytology by Edmund J. Sullivan
Cover of the book Business Cycles in the Run of History by Edmund J. Sullivan
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy