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In the 1960s, the Kalman filter was applied to navigation for the Apollo Project, which required estimates of the trajectories of manned spacecraft going to the Moon and back. It is now used in so many quantitative fields as to make it impractical to list them all in a summary: navigation and global positioning, tracking, guidance, robotics, computer vision, signal processing, voice recognition, video stabilization, automotive control systems, time-series analysis, econometrics to name but some!

During this talk, you will explore an intuitive introduction to the Kalman filter (not a covariance matrix in sight - well maybe one) and show how data kinds / type level literals can be used to create a filtering and smoothing library in Haskell that has some guarantees that it really does what it says on the tin.

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