BYU CS classes

To understand state space models, and in particular, the Kalman filter.

For this lab, you will implement the Kalman filter algorithm on a custon dataset. Your notebook should produce a visualization of the observations, the true state, the estimated state, and your estimate of the variance of your state estimates.

Your notebook will be graded on the following elements:

• 10% Data is correctly mean-centered
• 20% Correctly updates responsibilities
• 20% Correctly updates means
• 20% Correctly updates covariances
• 20% Correctly updates mixing weights
• 10% Final plot is tidy and legible

For this lab, you will perform simple target tracking using the random accelerations model. The model is therefore given by

# the dynamics model
 
# the observation model
 
# the dynamics noise
 
# the observation noise
 
# the initial state
 
mu_t =