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000727169 020__ $$a9783319175577$$qelectronic book
000727169 020__ $$a3319175572$$qelectronic book
000727169 020__ $$z9783319175560
000727169 0247_ $$a10.1007/978-3-319-17557-7$$2doi
000727169 035__ $$aSP(OCoLC)ocn909367838
000727169 035__ $$aSP(OCoLC)909367838
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000727169 049__ $$aISEA
000727169 050_4 $$aQC242.2
000727169 08204 $$a620.2/5$$223
000727169 1001_ $$aSullivan, Edmund J.,$$eauthor.
000727169 24510 $$aModel-based processing for underwater acoustic arrays$$h[electronic resource] /$$cEdmund J. Sullivan.
000727169 264_1 $$aCham :$$bSpringer,$$c2015.
000727169 300__ $$a1 online resource (x, 113 pages) :$$billustrations.
000727169 336__ $$atext$$btxt$$2rdacontent
000727169 337__ $$acomputer$$bc$$2rdamedia
000727169 338__ $$aonline resource$$bcr$$2rdacarrier
000727169 4901_ $$aSpringerBriefs in physics,$$x2191-5423
000727169 504__ $$aIncludes bibliographical references and index.
000727169 5050_ $$aPart I Introduction and Fundamentals -- Introduction -- Background Information -- Part II Applications -- Model-Based Array Processors: Stationary Arrays -- Model-Based Array Processors: Moving Arrays.
000727169 506__ $$aAccess limited to authorized users.
000727169 520__ $$aThis 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.
000727169 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed May 21, 2015).
000727169 650_0 $$aUnderwater acoustics$$xMathematical models.
000727169 650_0 $$aUnderwater acoustics$$xData processing.
000727169 77608 $$iPrint version:$$z9783319175560
000727169 830_0 $$aSpringerBriefs in physics.
000727169 852__ $$bebk
000727169 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-17557-7$$zOnline Access$$91397441.1
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000727169 983__ $$aOnline
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