Machine learningNonlinear Estimation

Extended Kalman Filter

The Extended Kalman Filter (EKF) is the nonlinear generalization of the Kalman Filter, extending the linear state estimation algorithm to nonlinear systems through local linearization. Developed by Bucy in the early 1960s, the EKF has become the workhorse for state estimation in nonlinear systems across robotics, aerospace, and navigation, enabling real-time processing of noisy measurements from nonlinear sensors and dynamics.

Open in MethodMindSoonVideoSoon

Read the full method

Members only

Sign in with a free account to read this section.

Sign in

Sources

  1. Bucy, R. S. (1961). A linear approximation to the solution of nonlinear filtering equations. Technical Report No. 32-486, Jet Propulsion Laboratory. link
  2. Bar-Shalom, Y., Li, X. R., & Kirubarajan, T. (2001). Estimation with Applications to Tracking and Navigation. Wiley-Interscience. DOI: 10.1002/0471221546
  3. Welch, G., & Bishop, G. (2006). An Introduction to the Kalman Filter. UNC-CH Technical Report. link

Related methods

Referenced by

ScholarGateExtended Kalman Filter (Extended Kalman Filter). Retrieved 2026-06-04 from https://scholargate.app/en/control-theory/extended-kalman-filter