Understanding Kalman Filters, Part 6: How to Use a Kalman Filter in Simulink
This video demonstrates how you can estimate the angular position of a simple pendulum system using a Kalman filter in Simulink.
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Using MATLAB and Simulink, you can implement linear time-invariant or time-varying Kalman filters. In this video, a simple pendulum system is modeled in Simulink using Simscape Multibody™. The angular position of the pendulum is estimated using the Kalman filter block that is available in Control System Toolbox™. The video shows how to configure Kalman filter block parameters such as the system model, initial state estimates, and noise characteristics.
Part 1: Why Use Kalman Filters?
Part 2: State Observers
Part 3: Optimal State Estimator
Part 4: Optimal State Estimator Algorithm
Part 5: Nonlinear State Estimators
Part 6: How to Use a Kalman Filter in Simulink
Part 7: How to Use an Extended Kalman Filter in Simulink
Part 3: Optimal State Estimator
Part 4: Optimal State Estimator Algorithm
Part 5: Nonlinear State Estimators
Part 6: How to Use a Kalman Filter in Simulink
Part 7: How to Use an Extended Kalman Filter in Simulink
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