State Estimation
State estimation is a mathematical process used to infer the internal state of a dynamic system from noisy, incomplete, or indirect measurements. It involves combining sensor data with system models to produce optimal estimates of unobservable variables, such as position, velocity, or temperature. This is fundamental in fields like robotics, aerospace, and control systems for real-time decision-making.
Developers should learn state estimation when building systems that require accurate real-time tracking or prediction, such as autonomous vehicles, drones, or industrial automation. It's essential for handling sensor noise, latency, and missing data in applications like navigation, target tracking, and process monitoring, enabling robust performance in uncertain environments.