Learning Goal: You should know and be able to use the Cramer-Rao Bound to understand fundamental limits on how well you can learn parameters $LaTeX: \theta$ from data LaTeX: y obtained with probability $LaTeX: p(y;\theta)$ . You should be able to describe and use the nonlinear Bayesian Filter and the Kalman filter. You should also be able to describe and use a particle filter.

Lecture Slides: Lecture1314_BayesianEstimation.pdf

Lecture Video: LECTURE1314_FULL.mp4 (from 2020)

Also available on youtube here

https://youtu.be/EJ8E_gDFFgE

Reading Assignment: Lecture notes on Bayesian Estimation and Sensor Fusion

Code:

positionCRB.m

gps.m

gps_tradeoff.m

PF.m

Please also check out the short video describing the particle filter available in the additional material:

Additional material - 13-14 Bayesian Estimation and Sensor Fusion