State estimation and localization
Kalman filtering, extended Kalman filtering, and data association in simulation.
This course project investigates how to estimate a moving system’s state from noisy observations. It covers Kalman filters, extended Kalman filters, landmark localization, and data association.
The simulations made uncertainty visible and showed why a good model matters as much as the update equations. The repository contains code and analysis from the assignment.