Kalman Filtering is a mathematical technique 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 process is particularly useful in applications like navigation, robotics, and finance, where data can be uncertain or incomplete. The filter operates in two main steps: prediction and update. In the prediction step, it uses the current state to forecast the next state of the system. In the update step, it adjusts this prediction based on new measurements, effectively reducing uncertainty and improving accuracy over time.