A comprehensive introduction to the Kalman Filter algorithm, explained through a practical radar tracking example. Covers the core predict-update cycle, including state extrapolation, covariance propagation, Kalman gain computation, and state/covariance update equations. The guide walks through two full iterations with numerical values, explaining measurement noise, process noise, and why combining predictions with measurements yields lower uncertainty than either alone. Three learning paths are offered: a single-page overview, a free web tutorial, and a paid book covering nonlinear filters and sensor fusion.
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