State Estimation using Moving Horizon Estimation and Particle Filtering

IAM Seminar
March 21, 2016 10:00 pm

Speaker:  James B. Rawlings, Chemical and Biological Engineering, Wisconsin-Madison

URL for Speaker:

Location:  LSK 460

Intended Audience:  Public

This seminar provides an overview of currently available methods for state estimation of linear, constrained and nonlinear dynamic systems. The seminar begins with a brief overview of the Kalman filter, which is the optimal estimator for a linear dynamic system subject to independent, normally distributed disturbances. Next, alternatives for treating nonlinear and constrained dynamic systems are discussed. Two complementary methods are presented in some detail: moving horizon estimation, which is based on optimization, and particle filtering, which is based on sampling. The advantages and disadvantages of these two approaches are presented. Topics for new research are suggested that address combining the best features of moving horizon estimators and particle filters.

Tea before the talk in the IAM lounge, LSK 306.