As a follow-on course to "Linear Kalman Filter Deep Dive", this course derives the steps of the extended Kalman filter and the sigma-point Kalman filter for estimating the state of nonlinear dynamic systems. You will learn how to implement these filters in Octave code and compare their results. You will be introduced to adaptive methods to tune Kalman-filter noise-uncertainty covariances online. You will learn how to estimate the parameters of a state-space model using nonlinear Kalman filters.
Overview
Syllabus
- The extended Kalman filter
- This week, you will learn how to implement the extended Kalman filter to estimate the state of a nonlinear system.
- The sigma-point (unscented) Kalman filter
- This week, you will learn how to implement the sigma-point Kalman filter to estimate the state of a nonlinear system.
- Extensions and refinements to nonlinear Kalman filters
- This week, you will learn how to extend and refine nonlinear Kalman filters for special cases.
- Parameter estimation and joint estimation
- This week, you will learn how to use nonlinear Kalman filters to estimate model parameter values.
Taught by
Gregory Plett
