The Kalman filter is a mathematical algorithm used to estimate the state of a dynamic system from a series of noisy measurements. It combines predictions from a model with actual measurements to produce more accurate estimates. This is particularly useful in fields like robotics, navigation, and finance, where precise tracking is essential. The filter operates in two main steps: prediction and update. In the prediction step, it estimates the future state based on the current state and a model of the system. In the update step, it adjusts this estimate using new measurements, effectively reducing uncertainty and improving accuracy over time.