東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Detection of random signals in depen...

Gualtierotti, Antonio F.

Linked to FindBook

Google Book

Amazon

博客來

Detection of random signals in dependent Gaussian noise

Record Type:	Electronic resources : Monograph/item
Title/Author:	Detection of random signals in dependent Gaussian noise/ by Antonio F. Gualtierotti.
Author:	Gualtierotti, Antonio F.
Published:	Cham :Springer International Publishing : : 2015.,
Description:	xxxiv, 1176 p. :ill., digital ;24 cm.
[NT 15003449]:	Prolog -- Part I: Reproducing Kernel Hilbert Spaces -- Part II: Cramer-Hida Representations -- Part III: Likelihoods -- Credits and Comments -- Notation and Terminology -- References -- Index.
Contained By:	Springer eBooks
Subject:	Random noise theory. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-22315-5
ISBN:	9783319223155$q(electronic bk.)

Detection of random signals in dependent Gaussian noise
Gualtierotti, Antonio F.

Detection of random signals in dependent Gaussian noise[electronic resource] /by Antonio F. Gualtierotti. - Cham :Springer International Publishing :2015. - xxxiv, 1176 p. :ill., digital ;24 cm.

Prolog -- Part I: Reproducing Kernel Hilbert Spaces -- Part II: Cramer-Hida Representations -- Part III: Likelihoods -- Credits and Comments -- Notation and Terminology -- References -- Index.

The book presents the necessary mathematical basis to obtain and rigorously use likelihoods for detection problems with Gaussian noise. To facilitate comprehension the text is divided into three broad areas - reproducing kernel Hilbert spaces, Cramer-Hida representations and stochastic calculus - for which a somewhat different approach was used than in their usual stand-alone context. One main applicable result of the book involves arriving at a general solution to the canonical detection problem for active sonar in a reverberation-limited environment. Nonetheless, the general problems dealt with in the text also provide a useful framework for discussing other current research areas, such as wavelet decompositions, neural networks, and higher order spectral analysis. The structure of the book, with the exposition presenting as many details as necessary, was chosen to serve both those readers who are chiefly interested in the results and those who want to learn the material from scratch. Hence, the text will be useful for graduate students and researchers alike in the fields of engineering, mathematics and statistics.

ISBN: 9783319223155$q(electronic bk.)

Standard No.: 10.1007/978-3-319-22315-5doiSubjects--Topical Terms:

651135
Random noise theory.

LC Class. No.: TK5102.5

Dewey Class. No.: 003.54

Detection of random signals in dependent Gaussian noise
LDR:02305nmm a2200325 a 4500 001 2016501
003 DE-He213
005 20160512150326.0
006 m d
007 cr nn 008maaau
008 160613s2015 gw s 0 eng d
020 $a 9783319223155$q(electronic bk.)
020 $a 9783319223148$q(paper)
024 7 $a 10.1007/978-3-319-22315-5 $2 doi
035 $a 978-3-319-22315-5
040 $a GP $c GP
041 0 $a eng
050 4 $a TK5102.5
072 7 $a PBT $2 bicssc
072 7 $a PBWL $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 003.54 $2 23
090 $a TK5102.5 $b .G912 2015
100 1 $a Gualtierotti, Antonio F. $3 2165627
245 1 0 $a Detection of random signals in dependent Gaussian noise $h [electronic resource] / $c by Antonio F. Gualtierotti.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a xxxiv, 1176 p. : $b ill., digital ; $c 24 cm.
505 0 $a Prolog -- Part I: Reproducing Kernel Hilbert Spaces -- Part II: Cramer-Hida Representations -- Part III: Likelihoods -- Credits and Comments -- Notation and Terminology -- References -- Index.
520 $a The book presents the necessary mathematical basis to obtain and rigorously use likelihoods for detection problems with Gaussian noise. To facilitate comprehension the text is divided into three broad areas - reproducing kernel Hilbert spaces, Cramer-Hida representations and stochastic calculus - for which a somewhat different approach was used than in their usual stand-alone context. One main applicable result of the book involves arriving at a general solution to the canonical detection problem for active sonar in a reverberation-limited environment. Nonetheless, the general problems dealt with in the text also provide a useful framework for discussing other current research areas, such as wavelet decompositions, neural networks, and higher order spectral analysis. The structure of the book, with the exposition presenting as many details as necessary, was chosen to serve both those readers who are chiefly interested in the results and those who want to learn the material from scratch. Hence, the text will be useful for graduate students and researchers alike in the fields of engineering, mathematics and statistics.
650 0 $a Random noise theory. $3 651135
650 1 4 $a Mathematics. $3 515831
650 2 4 $a Probability Theory and Stochastic Processes. $3 891080
650 2 4 $a Functional Analysis. $3 893943
650 2 4 $a Information and Communication, Circuits. $3 893882
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-22315-5
950 $a Mathematics and Statistics (Springer-11649)