State estimation is a process used in various fields, such as engineering and robotics, to determine the internal state of a system based on available measurements. It involves using mathematical models and algorithms to infer the most likely state of a system when direct measurement is not possible or is noisy. One common method for state estimation is the Kalman filter, which combines predictions from a model with actual measurements to produce a more accurate estimate. This technique is widely used in applications like navigation, control systems, and signal processing to enhance performance and reliability.