Kalman Filter Variants

Kalman filter variants are mathematical methods used to estimate the true state of a system from noisy measurements. The standard Kalman filter works well for linear systems with Gaussian noise, providing optimal estimates. Variants like the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) handle nonlinear systems by approximating or sampling the system's behavior more accurately. Other versions, such as the Unscented Kalman Filter or Particle Filter, address complexities like multiple possible states or non-Gaussian noise, improving estimation in challenging environments. These filters help in navigation, robotics, and tracking by filtering out uncertainties in real-time.