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, Cramér-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.
Rezensionen / Stimmen
"The book rigorously develops the mathematical foundations to assess the possible likelihoods for detection problems with Gaussian noise. . The text would be very useful for graduate students and researchers alike in the fields of mathematics, engineering and statistics." (John Schmeelk, zbMATH 1364.60004, 2017)
Sprache
Verlagsort
Verlagsgruppe
Springer International Publishing
Illustrationen
5
5 s/w Abbildungen
XXXIV, 1176 p. 5 illus.
Dateigröße
ISBN-13
978-3-319-22315-5 (9783319223155)
DOI
10.1007/978-3-319-22315-5
Schweitzer Klassifikation
Prolog.- Part I: Reproducing Kernel Hilbert Spaces.- Part II: Cramér-Hida Representations.- Part III: Likelihoods.- Credits and Comments.- Notation and Terminology.- References.- Index.
