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Fundamentals of Stochastic Signals, Systems and Estimation Theory (third edition) explains the concepts underlying modeling and analysis of stochastic signals and linear stochastic systems. Two popular stochastic models: the polynomial (or transfer-function) model and the state-space model, are employed in schemes that lead to the successful estimation of unknown signal-/system-model parameters or states. The third edition is a substantially expanded treatment that benefits from new work performed in nonlinear robust estimation and features:
- new chapters related to the use of nonlinear filtering as an alternative to extended and linearized Kalman filtering and
- novel practical examples related to video-based object tracking and radar-based target tracking for following maneuvers.
Many examples are used to illustrate key concepts in an intuitive fashion and readers are shown how to write software implementations of estimators. Experiments and simulations are performed using MATLAB® to help readers to understand the main theoretical concepts.
The book will help professionals and students studying high-order dynamic systems with random inputs. It will be of use to readers working with or studying problems in wireless communications, networking, electronics, photonics, power systems, robotics, and mechatronics.
List of contents
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 Estimation.- Optimum Nonrecursive Linear Estimation: Wiener Filtering.- Optimum Recursive Linear Estimation: Kalman Filtering.- Extensions of The Optimum Recursive (Kalman) Filter.
About the author
Branko Kovačević is a Senior Member of the IEEE; he received the Ph.D. degree from the University of Belgrade, Belgrade, Serbia, in 1984. In 1981, he joined the Faculty of Electrical Engineering, University of Belgrade, where he is currently Professor Emeritus. He is the author of eight books and more than 80 articles in scientific journals. His current research interests include robust estimation, system identification, adaptive and nonlinear filtering, optimal and adaptive control, and digital signal processing. He is a Member of the EURASIP, the WSAES, and the National Association ETRAN and a Corresponding Member of the Academy of Engineering Sciences of Serbia. He was the Recipient of the Outstanding Research Prize of the Institute of Applied Mathematics and Electronics, the Prize of the Serbian Association for Informatics, and the Prize of the Association of Radio Systems Engineers. He is Reviewer of IEEE Transactions on Automatic Control, the IFAC journal Automatica, and Signal Processing.
Željko Đurović received his Ph.D. degree from the Faculty of Electrical Engineering, University of Belgrade, Yugoslavia, in 1994. His M.Sc. was from the field of multiple target tracking (supervised by Prof. Srđan Stanković), while his Ph.D. was from the field of estimation theory and supervised by Prof. Branko Kovačević.
Summary
Fundamentals of Stochastic Signals, Systems and Estimation Theory (third edition) explains the concepts underlying modeling and analysis of stochastic signals and linear stochastic systems. Two popular stochastic models: the polynomial (or transfer-function) model and the state-space model, are employed in schemes that lead to the successful estimation of unknown signal-/system-model parameters or states. The third edition is a substantially expanded treatment that benefits from new work performed in nonlinear robust estimation and features:
- new chapters related to the use of nonlinear filtering as an alternative to extended and linearized Kalman filtering and
- novel practical examples related to video-based object tracking and radar-based target tracking for following maneuvers.
Many examples are used to illustrate key concepts in an intuitive fashion and readers are shown how to write software implementations of estimators. Experiments and simulations are performed using MATLAB® to help readers to understand the main theoretical concepts.
The book will help professionals and students studying high-order dynamic systems with random inputs. It will be of use to readers working with or studying problems in wireless communications, networking, electronics, photonics, power systems, robotics and mechatronics.
