Example Concept: Kalman filtering is a popular technique for sensor fusion, especially in dynamic environments. It uses a series of measurements observed over time, containing noise and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those based on a single measurement alone. The filter operates recursively on streams of noisy input data to produce a statistically optimal estimate of the underlying system state.