Kalman Filter
The Kalman filter is a mathematical algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, to produce estimates of unknown variables that tend to be more accurate than those based on a single measurement alone. It operates recursively on streams of noisy input data to estimate the state of a dynamic system, such as in navigation, control systems, and signal processing. The filter combines predictions from a system model with new measurements to update the state estimate and its uncertainty.
Developers should learn Kalman filtering when working on applications involving real-time state estimation, sensor fusion, or tracking in fields like robotics, autonomous vehicles, and aerospace. It is particularly useful for handling noisy sensor data, such as GPS, IMU, or lidar readings, to improve accuracy in position, velocity, or orientation estimates. For example, in a drone navigation system, a Kalman filter can combine accelerometer and gyroscope data to provide a stable and precise attitude estimate.