Fundamentals of stochastic signals, systems and estimation theory: with worked examples

The main theme of this book deals with fundamental concepts underlying stochastic signal or linear stochastic systems, their modelling and analysis as wellas model-based signal processing. Two popular stochastic models, the polynomial (or transfer function) model and the state space model are employed in schemes that lead to the estimation of unknown system parameters or states. The book is written for undergraduate and graduate students as well as practising engineers, specializing the the areas of electrical communications, signal processing and automatic control. Many examples illustrate the concepts of this book and the reader learns how to write software implementations of estimators oncomputers. INDICE: Review of the theory of probability and random variables.- Fundamentals of stochastic processes.- Linear discrete-time stochastic systems.- Linear continuous-time stochastic systems.- Fundamentals of estimations.- Optimum nonrecursive linear estimation: Wiener filtering.- Optimum nonrecursive linearestimation: Kalman filtering.- Extensions of the optimum Kalman filter.- Appendices.

• ISBN: 978-3-540-70990-9
• Editorial: Springer
• Encuadernacion: Cartoné
• Páginas: 380
• Fecha Publicación: 01/07/2008
• Nº Volúmenes: 1
• Idioma: Inglés