Applied State Estimation (EE 67033)
University of Notre Dame
Description:
This course covers techniques used in estimating the state of a dynamical system. The course reviews basic concepts in linear systems, Bayesian estimation, and minimum mean-square estimation followed by the introduction of the conventional Kalman filter in both discrete-time and continuous-time formats. The course examines extensions of the Kalman filter that include the extended and unscented Kalman filter as well as the H-infinity filter. The course may also cover some advanced topics in Multi-target tracking, state estimation over networks, and the use of Markov Chain Monte Carlo (MCMC) methods. (Spring)
Topics:
- Linear Systems Theory and Deterministic Least Squares Problem
- Random Processes and Stochastic Least Square Problem
- Discrete-time Kalman Filter
- Square-Root Filter Implementations
- Continuous-time Kalman Filter
- H-infinity Filter
- Extended and Unscented Kalman Filter
- Probabilistic Data-Association Filter (PDAF)
- Distributed Kalman Filtering over Networks
- Particle Filtering
Grading: 50% midterm, 50% project.
Instructors:
- Michael Lemmon, Dept. of Electrical Engineering,
University of Notre Dame (lemmon at nd dot edu)
-
Yih-Fang Huang, Dept. of Electrical Engineering, University of Notre
Dame, (huang at nd dot edu)
Textbooks:
- required: D. Simon, "Optimal State Estimation: Kalman,
H-infinity, and nonlinear approaches, Wiley-Interscience, 2006, ISBN-10-0471708585
- optional: T. Kailath, A.H. Ssayed, and B. Hassibi, Linear
Estimation, Prentice-Hall (2000), ISBN-10-0130224642