Detection of Abrupt Changes: Theory and Application
by Michele Basseville, Igor V. Nikiforov
Publisher: Prentice-Hall 1993
Number of pages: 469
This book presents mathematical tools and techniques for solving change detection problems in wide domains like signal processing, controlled systems and monitoring. The book covers a wide class of stochastic processes, from scalar independent observations to multidimensional dependent ARMA and state-space models, the properties of the algorithms for statistical change detection, tuning and optimizing change detection in real-world applications. The book is intended for engineers and researchers involved in signal processing, and others.
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In this volume the author covered what should be standard topics in a course of parametric estimation: Bayes estimates, unbiased estimation, Fisher information, Cramer-Rao bounds, and the theory of maximum likelihood estimation.
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A valuable reference both for signal processing specialists seeking to apply their expertise in the rapidly growing wireless communications field, and for communications specialists eager to exploit signal processing techniques.
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The aim of this book is to present graduate students with a thorough survey of reference probability models and their applications to optimal estimation and control. Readers are assumed to have basic grounding in probability and systems theory.
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In this edition the emphasis is on real-time adaptive signal processing, eigenvector methods of spectrum estimation, and parallel processor implementations of optimum filtering and prediction algorithms, and including several new developments.