Main Content

Online State Estimation

Estimate model parameters using linear and nonlinear Kalman filters at the command line and in Simulink®

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.

Functions

extendedKalmanFilterCreate extended Kalman filter object for online state estimation
unscentedKalmanFilterCreate unscented Kalman filter object for online state estimation
particleFilterParticle filter object for online state estimation
correctCorrect state and state estimation error covariance using extended or unscented Kalman filter, or particle filter and measurements
residualReturn measurement residual and residual covariance when using extended or unscented Kalman filter
predictPredict state and state estimation error covariance at next time step using extended or unscented Kalman filter, or particle filter
initializeInitialize the state of the particle filter
cloneCopy online state estimation object

Blocks

Kalman FilterEstimate states of discrete-time or continuous-time linear system
Extended Kalman FilterEstimate states of discrete-time nonlinear system using extended Kalman filter
Particle FilterEstimate states of discrete-time nonlinear system using particle filter
Unscented Kalman FilterEstimate states of discrete-time nonlinear system using unscented Kalman filter

Topics

Online Estimation Basics

Online State Estimation in Simulink

Online State Estimation at the Command Line

Troubleshooting

Troubleshoot Online State Estimation

Troubleshoot online state estimation performed using extended and unscented Kalman filter algorithms.