Kalman Filter

The Kalman filter is the workhorse of state estimation: given a stream of noisy measurements, it produces a statistically optimal estimate of a system’s true underlying state by alternating between predicting the state forward in time and correcting that prediction with each new observation. It powers everything from GPS navigation to spacecraft guidance.

This project is a from-scratch NumPy implementation of the Kalman filter, with worked examples and visualizations showing how the filter tracks a system’s state through noise — how the prediction and update steps interact, and how the estimate converges as observations accumulate.

See the repository for the implementation and examples.