Kalman filtering is a mathematical technique used in estimation and control systems to predict the state of a system based on noisy measurements. It was developed by Rudolf E. Kálmán in the 1960s and is widely used in various fields such as engineering, computer science, and economics. The basic idea behind Kalman filtering is to combine information from previous estimates of the system's state with new measurements to generate an optimal estimate of the current state. The filter takes into account the uncertainty in the measurements and uses a set of equations to update the state estimate in a recursive manner. Kalman filtering is particularly useful in situations where the system dynamics are linear and the noise in the measurements is Gaussian. It has applications in navigation systems, signal processing, robotics, and many other areas where accurate estimation of a system's state is crucial.