What is a Kalman Filter?
The Kalman Filter is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone.
Why is it Special?
It's used in everything from spacecraft navigation and GPS, to financial modeling and self-driving cars. It blends prediction and correction elegantly, continuously updating its estimate of the state based on new observations.
Core Concept
The filter estimates the state of a system using two steps: Prediction and Update. First, it predicts the next state using a motion model. Then it corrects the prediction with actual measurements, weighting them by their uncertainty.
Applications
You can find Kalman Filters in robotics, economics, signal processing, and more. They're ideal when you need to infer unobservable variables based on noisy data.