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Understanding Kalman Filters and MATLAB Designing

Part 1: Why Use Kalman Filters?

A Kalman filter is an optimal estimation algorithm used to estimate states of a system from indirect and uncertain measurements.

Video Lecture: 1

Part 2: State Observers

Learn the working principles of state observers, and discover the math behind them. State observers are used to estimate the internal states of a system when you can’t directly measure them.

Video Lecture: 2 

Part 3: Optimal State Estimator

Kalman filters combine two sources of information, the predicted states and noisy measurements, to produce optimal, unbiased estimates of system states. The filter is optimal in the sense that it minimizes the variance in the estimated states.

Video Lecture: 3

Part 4: Optimal State Estimator Algorithm

Discover the set of equations you need to implement a Kalman filter algorithm. You’ll learn how to perform the prediction and update steps of the Kalman filter algorithm, and you’ll see how a Kalman gain incorporates the predicted state estimate (a priori state estimate) and the measurement in order to calculate the new state estimate (a posteriori state estimate).

Video Lecture: 4

Part 5: Nonlinear State Estimators

The basic concepts behind nonlinear state estimators, including extended Kalman filters, unscented Kalman filters, and particle filters.

Video Lecture: 5

Part 6: How to Use a Kalman Filter in Simulink

Estimate the angular position of a simple pendulum system using a Kalman filter in Simulink.

Video Lecture: 6

Part 7: How to Use an Extended Kalman Filter in Simulink

simple pendulum system is modelled in Simulink using Simscape Multibody™. The angular position of the nonlinear pendulum system is estimated using the Extended Kalman Filter block that is available in Control System Toolbox™. The video shows how to specify Extended Kalman Filter block parameters such as the state transition and measurement functions, initial state estimates, and noise characteristics. If you want to run state estimation on your hardware in real time, you can generate C/C++ code from the Extended Kalman Filter block in Simulink, and deploy it to your hardware.

Video Lecture: 7

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