Kalman Filter
The Kalman Filter is a mathematical algorithm used for estimating the state of a dynamic system from a series of noisy measurements. It operates recursively, combining predictions from a system model with new observations to produce optimal estimates in real-time. Widely applied in control systems, navigation, and signal processing, it minimizes the mean squared error of the estimated parameters.
Developers should learn the Kalman Filter when working on projects involving real-time data fusion, such as robotics, autonomous vehicles, or financial modeling, where accurate state estimation from uncertain sensor data is critical. It's essential for applications requiring noise reduction and prediction in dynamic environments, like GPS tracking, inertial navigation systems, or stock price forecasting.