Detection of Random Signals in Dependent Gaussian Noise

Nonfiction, Science & Nature, Mathematics, Functional Analysis, Statistics
Cover of the book Detection of Random Signals in Dependent Gaussian Noise by Antonio F. Gualtierotti, Springer International Publishing
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Author: Antonio F. Gualtierotti ISBN: 9783319223155
Publisher: Springer International Publishing Publication: December 15, 2015
Imprint: Springer Language: English
Author: Antonio F. Gualtierotti
ISBN: 9783319223155
Publisher: Springer International Publishing
Publication: December 15, 2015
Imprint: Springer
Language: English

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.

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

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.

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