The Kalman filter is an algorithm that estimates the states of a system from indirect and uncertain measurements. Kalman filters are widely used for applications such as navigation and tracking, control systems, signal processing, computer vision, and econometrics.
You can use MATLAB®, Simulink®, and Control System Toolbox™ to design and simulate linear steady-state and time-varying, extended, and unscented Kalman filter, or particle filter algorithms. Read this set of examples and code to learn more about:
- Kalman Filtering: steady-state and time-varying Kalman filter design and simulation in MATLAB
- State Estimation Using Time-Varying Kalman Filter: design of a navigation and tracking system in Simulink
- Estimate States of Nonlinear System with Multiple, Multirate Sensors: position and velocity estimation of an object with GPS and radar sensors operating at different sample rates
- Nonlinear State Estimation Using Unscented Kalman Filter and Particle Filter: nonlinear state estimation of a van der Pol oscillator from noisy measurements
- Nonlinear State Estimation of a Degrading Battery System: unscented and event-based Kalman filter design to estimate the nonlinear states of a lithium battery
- Tracking Maneuvering Target: tracking filter design using single motion and multiple motion models