EKF Meaning and
Definition
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Extended Kalman Filter (EKF) is a recursive state estimation algorithm used in the field of signal processing and control engineering. It is an extension of the traditional Kalman Filter (KF) and is designed to handle non-linear systems.
The EKF operates by estimating the current state of a dynamic system based on measurements obtained through sensors. The filter predicts the system's state at the next time step using the dynamic model of the system, and then updates the prediction using the measurements obtained at that time step. This prediction-update process is iterated recursively to provide an accurate estimate of the system's state.
Unlike the KF, which assumes linearity in both the system dynamics and measurement functions, the EKF can handle non-linear functions by approximating them with a locally linear approximation. This is achieved by linearizing the system and measurement equations at each time step using first-order Taylor series expansion. The EKF then utilizes these linear approximations to propagate the state estimation and its associated error covariance.
The EKF is widely used in various applications, such as robotics, navigation systems, and control systems, where the underlying system models are non-linear. Its ability to deal with non-linearities makes it a valuable tool in situations where traditional linear Kalman filters are inadequate. However, it is important to note that the EKF is an approximation technique and may be sensitive to errors or uncertainties in the linearization process.
Common Misspellings for EKF
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