Estimación de estados online
Realice la estimación de parámetros de modelos utilizando filtros de Kalman lineales y no lineales en la línea de comandos y en Simulink®
Puede estimar los estados de su sistema con datos en tiempo real y algoritmos de filtros de Kalman lineales, extendidos o unscented. Puede realizar una estimación de estados online utilizando los bloques de Simulink en la subbiblioteca Estimators de la biblioteca System Identification Toolbox™. Después, puede generar código C/C++ para estos bloques con Simulink Coder™ e implementar este código en una plataforma objetivo integrada. También puede realizar una estimación online de estados en la línea de comandos e implementar el código con MATLAB® Compiler™ o MATLAB Coder.
Funciones
extendedKalmanFilter | Create extended Kalman filter object for online state estimation |
unscentedKalmanFilter | Create unscented Kalman filter object for online state estimation |
particleFilter | Particle filter object for online state estimation |
correct | Correct state and state estimation error covariance using extended or unscented Kalman filter, or particle filter and measurements |
residual | Return measurement residual and residual covariance when using extended or unscented Kalman filter |
predict | Predict state and state estimation error covariance at next time step using extended or unscented Kalman filter, or particle filter |
initialize | Initialize the state of the particle filter |
clone | Copy online state estimation object |
generateJacobianFcn | Generate MATLAB Jacobian functions for extended Kalman filter using automatic differentiation (Desde R2023a) |
Bloques
| Kalman Filter | Estimate states of discrete-time or continuous-time linear system |
| Extended Kalman Filter | Estimate states of discrete-time nonlinear system using extended Kalman filter |
| Particle Filter | Estimate states of discrete-time nonlinear system using particle filter |
| Unscented Kalman Filter | Estimate states of discrete-time nonlinear system using unscented Kalman filter |
Temas
Conceptos básicos de la estimación online
- What Is Online Estimation?
Estimate states and parameters of a system in real-time. - Extended and Unscented Kalman Filter Algorithms for Online State Estimation
Description of the underlying algorithms for state estimation of nonlinear systems.
Estimación de estados online en Simulink
- Estimación de estados con el filtro de Kalman variante en el tiempo
Realice la estimación de los estados de sistemas lineales con filtros de Kalman variantes en el tiempo en Simulink. - Estimate States of Nonlinear System with Multiple, Multirate Sensors
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 in Simulink
Validate online state estimation that is performed using Extended Kalman Filter and Unscented Kalman Filter blocks. - Parameter and State Estimation in Simulink Using Particle Filter Block
This example demonstrates the use of the Particle Filter block in System Identification Toolbox™. - State Estimation with Wrapped Measurements Using Extended Kalman Filter
This example shows how to use the extended Kalman filter algorithm for nonlinear state estimation for 3D tracking involving circularly wrapped angle measurements.
Estimación de estados online en la línea de comandos
- Nonlinear State Estimation Using Unscented Kalman Filter and Particle Filter
Use the unscented Kalman filter algorithm for nonlinear state estimation for the van der Pol oscillator. - Validate Online State Estimation at the Command Line
Validate online state estimation that is performed using extended and unscented Kalman filter algorithms. - Generate Code for Online State Estimation in MATLAB
Deploy extended or unscented Kalman filters, or particle filters using MATLAB Coder software.
Solución de problemas
Troubleshoot Online State Estimation
Troubleshoot online state estimation performed using extended and unscented Kalman filter algorithms.
