You can estimate the states of your system using real-time data and linear, extended, or unscented Kalman filter algorithms. You can perform online state estimation using the Simulink blocks in the Estimators sublibrary of the System Identification Toolbox™ library. You can then generate C/C++ code for these blocks using Simulink Coder™, and deploy this code to an embedded target. You can also perform online state estimation at the command line, and deploy your code using MATLAB® Compiler™ or MATLAB Coder.
|Create extended Kalman filter object for online state estimation|
|Create unscented Kalman filter object for online state estimation|
|Particle filter object for online state estimation|
|Correct state and state estimation error covariance using extended or unscented Kalman filter, or particle filter and measurements|
|Predict state and state estimation error covariance at next time step using extended or unscented Kalman filter, or particle filter|
|Initialize the state of the particle filter|
|Copy online state estimation object|
Estimate states and parameters of a system in real-time.
Description of the underlying algorithms for state estimation of nonlinear systems.
Estimate states of linear systems using time-varying Kalman filters in Simulink.
Use an Extended Kalman Filter block to estimate the states of a system with multiple sensors that are operating at different sampling rates.
Validate online state estimation that is performed using Extended Kalman Filter and Unscented Kalman Filter blocks.
Use the unscented Kalman filter algorithm for nonlinear state estimation for the van der Pol oscillator.
You can use an extended Kalman filter for fault detection.
Validate online state estimation that is performed using extended and unscented Kalman filter algorithms.
Deploy extended or unscented Kalman filters, or particle filters using MATLAB Coder software.
Troubleshoot online state estimation performed using extended and unscented Kalman filter algorithms.