Kalman filter

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For statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, who was one of the primary developers of its theory.

Kalman filter - Wikipedia Jump to content From Wikipedia, the free encyclopedia Algorithm that estimates unknowns from a series of measurements over time The Kalman filter keeps track of the estimated state of the system and the variance or uncertainty of the estimate. The estimate is updated using a state transition model and measurements. x ^ k ∣ k − 1 {\displaystyle {\hat {x}}_{k\mid k-1}} denotes the estimate of the system's state at time step k before the k -th measurement y k has been taken into account; P k ∣ k − 1 {\displaystyle P_{k\mid k-1}} is the

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