Stochastic Signal Processing.pdf
This book intends to provide graduate students in electrical and information science a solid background in stochastic signal processing. Chapter one introduces random signals through measurement noise. Chapter two develops fundamental concepts in probability theory and statistical methods. Chapter three is devoted to stochastic processes, stochastic system theory, and statistical signal processing. The examples are carefully selected. Some of them are aimed at motivating students interested in advanced topics such as signal detection, estimation, spectral analysis and system identification. Problems with solutions and MATLAB exercises are included to encourage self study by researchers or engineers in related areas. The most important concepts in statistics are presented so that linear systems and nonlinear ones as rectifiers with random input and output signals have proper mathematical description and allow statistical inference. Such systems are fundamental to many engineering areas, for example, electronics, measurements, communications and control.
Pei-Jung Chung received Dr.-Ing. in 2002 from Ruhr-Universitat Bochum, Germany with distinction. In 2006 she joined the Institute for Digital Communications, School of Engineering, the University of Edinburgh, UK as Lecturer. Currently, she is Associate Member of IEEE Signal Processing Society Sensor Array Multichannel Technical Committee and serves for IEEE Communications Society, Multimedia Communications Technical Committee as Vice Chair of Interest Group on Acoustic and Speech Processing for Communications. Johann F. Bohme has been Professor of signal theory in the Department of Electrical Engineering and Information Sciences, Ruhr-Universitat Bochum, Germany, since 1980. His research interests are in the areas of statistical signal processing and its applications. He is IEEE Life Fellow and recipient of the 2003 IEEE Signal Processing Society Technical Achievement Award.
Introduction to statistics: probability theory .- statistical inferences .- Models for measured signals: stochastic signals .- stochastic processes .- system theory with stochastic signals .- system identification and spectrum analysis.