Applied State Estimation HomePage

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

Additional Readings:

S. Julier, J. Uhlmann and H. Durrant-Whyte, "A new approach for the nonlinear transformation of means and covariances in filters and estimators", IEEE Transactions on Automatic Control, Volume 45(3), pp. 477-482, March 2000.
S.J. Julier and J.K. Uhlmann, "Unscented Filtering and Nonlinear Estimation" , Proceedings of the IEEE, volume 92, no. 3, page 401-422, March 2004.
N. Gordan, D. Salmond, and A. Smith, "Novel approach to nonlinear/nonGuassian Bayesian state estimation" , IEE Proceedings-F, 140(2), pp. 107-113, April 1993.
A. Doucet, S. Godsill, and C. Andrieu, "On sequential Monte Carlo sampling methods for Bayesian filtering" , Statistics and Computing, volume 10, pages 197-208, 2000.