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 of a van der pol oscillator from noisy measurements
- : 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