A 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 applications like navigation, where sensor data can be unreliable. 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. In the update step, it adjusts this estimate using new measurements, weighing the uncertainty of both the model and the measurements to improve accuracy.