This week I worked on better understanding Kalman filters and implementing the filter in Python. I decided that since we have both a linear and and non-linear localization algorithm I was going to start by implementing linear Kalman filter, and get that working first and then move on to the extended Kalman filter. I have an implementation of Kalman filter that needs that models and covariances inputted as parameters.
While discussing Kalman filters, we decided that it may be best to use a python library instead of writing it from scratch, especially for the extended Kalman filter. Using a python library still requires us to determine what models to use and what the covariances are, so it lets us jump right to fine tuning these values and getting the best results as possible.
In the coming week I will be working on determining the best values for the state transition model, observation model, covariance of process noise, and covariance of observation noise. I hope to have the Kalman filter and then the extended Kalman filter working with our simulation by the end of the week.