Unscented Kalman Filter
The Unscented Kalman Filter (UKF) is a recursive Bayesian estimation algorithm used for state estimation in nonlinear dynamic systems. It improves upon the Extended Kalman Filter by using a deterministic sampling approach called the unscented transform to better approximate the mean and covariance of Gaussian random variables through nonlinear transformations. This makes it particularly effective for systems where linearization errors would degrade performance.
Developers should learn the UKF when working on state estimation problems in robotics, autonomous vehicles, or sensor fusion applications where system dynamics are nonlinear. It provides more accurate estimates than the Extended Kalman Filter for highly nonlinear systems without the computational burden of particle filters, making it ideal for real-time applications like tracking, navigation, and control systems.