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Record Nr. |
UNINA9910300244203321 |
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Autore |
Gualtierotti Antonio F |
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Titolo |
Detection of Random Signals in Dependent Gaussian Noise / / by Antonio F. Gualtierotti |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
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ISBN |
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Edizione |
[1st ed. 2015.] |
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Descrizione fisica |
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1 online resource (1198 p.) |
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Disciplina |
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Soggetti |
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Probabilities |
Functional analysis |
Information theory |
Probability Theory and Stochastic Processes |
Functional Analysis |
Information and Communication, Circuits |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Prolog -- Part I: Reproducing Kernel Hilbert Spaces -- Part II: Cramér-Hida Representations -- Part III: Likelihoods -- Credits and Comments -- Notation and Terminology -- References -- Index. |
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Sommario/riassunto |
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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 |
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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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