Detection of Random Signals in Dependent Gaussian Noise

by Antonio F. Gualtierotti (Author)
Derives a new likelihood function for solving basic problems in the theory of signal detection
Completes the Cramér-Hida representation theory
Investigates the scope of the signal-plus-noise model
Includes the necessary details to make the theory ready for application
References to statistical communication theory illustrate the power of the mathematics involved

From the Back Cover
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.

Number of Illustrations and Tables
5 illus.
Topics
Probability Theory and Stochastic Processes
Functional Analysis
Information and Communication, Circuits

More info and Hardcover at Springer

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