Optimal State Estimation and System Identification

Author: Wang Zhi-xian
Publisher:
Publish Date: 2004-06-01
Features: This book systematically elaborates on the fundamental concepts, theories, and methods of optimal state estimation and system identification. The book is divided into two parts, 14 chapters in total: Part I is Optimal State Estimation, which introduces the basic concepts of optimal estimation, Kalman filtering for linear systems, optimal linear smoothing, the stability of Kalman filtering, filter divergence and its mitigation methods, and nonlinear filtering. The second part is System Identification, which covers general concepts of system identification, impulse response methods and correlation function methods, least squares class identification methods, maximum likelihood methods and prediction error methods, time series models and stochastic approximation methods, identification of multivariable systems, and closed-loop system identification. The appendix provides some mathematical tools used in this course, such as matrix analysis. The book has a clear structure and highlights key points, emphasizing both the systematicity and completeness of theory while also focusing on engineering applicability. It can serve as a graduate textbook for students in automatic control and related disciplines in higher technical colleges, as well as a reference for researchers, engineers, and technicians in the field of automatic control.