Course Name：Estimation and Filtering
Credit：3
Prerequisites:Linear algebra、linear systems theory、probability theory and random process
Description:
This course covers the essential theory and methods on estimation and filtering. It can be applied in space engineering, communication, control system, signal processing, finance, etc. The prerequisites of this course include probability theory, random process, linear systems theory, linear algebra and mathematical statistics.
The contents of this course consist of review of prerequisites, basic concepts and methods (e.g., MMSE, LMMDE, LS, MLE, MAP) of estimation theory, the Kalman filtering and its analysis, the extension of the Kalman filtering, kimematic models for state estimation in target tracking, optimal prediction, optimal smoothing, nonlinear state estimation (e.g., EKF, UKF, CKF, PF), maneuvering target tracking and the application of estimation in navigation, multi-sensor estimation fusion, etc.
With this course, the students can master the essential theory and methods of estimation and filtering. This will help them build solid foundation in both theory and methods whenever estimation and filtering is needed in their future work.
