My final project in Bayesian Scientific Computing was designing a Kalman Filter Architecture flexible enough to be applied to anything from a car (my design team) to a rocket (my group mate’s design team). The result is a Python library that allows for modular combinations of Linear, Extended, and Unscented Kalman Filter Predictors and Correctors while handling sensor dropout and variable update rates.

Take a look at the poster

Or the full report