Video and Webinar Series

# Understanding Kalman Filters

Discover real-world situations in which you can use Kalman filters. Kalman filters are often used to optimally estimate the internal states of a system in the presence of uncertain and indirect measurements. Learn the working principles behind Kalman filters by watching the following introductory examples.

You will explore the situations where Kalman filters are commonly used. When the state of a system can only be measured indirectly, you can use a Kalman filter to optimally estimate the states of that system. And when measurements from different sensors are available but subject to noise, you can use a Kalman filter to combine sensory data from various sources (known as sensor fusion) to find the best estimate of the parameter of interest.

You will also learn about state observers by walking through a few examples that include simple math. This will help you understand what a Kalman filter is and how it works. At a high level, Kalman filters are a type of optimal state estimator. The videos also include a discussion of nonlinear state estimators, such as extended and unscented Kalman filters.

Finally, an example demonstrates how the states of a linear system can be estimated using Kalman filters, MATLAB®, and Simulink®

Discover common uses of Kalman filters by walking through some examples. A Kalman filter is an optimal estimation algorithm used to estimate states of a system from indirect and uncertain measurements.

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.

Learn how Kalman filters work. Kalman filters combine two sources of information, the predicted states and noisy measurements, to produce optimal, unbiased state estimates.

Discover the set of equations you need to implement the Kalman filter algorithm.

This video explains the basic concepts behind nonlinear state estimators, including extended Kalman filters, unscented Kalman filters, and particle filters.

Estimate the angular position of a simple pendulum system using a Kalman filter in Simulink. You will learn how to configure Kalman filter block parameters such as the system model, initial state estimates, and noise characteristics.

Estimate the angular position of a nonlinear pendulum system using an extended Kalman filter. You will learn how to specify Extended Kalman Filter block parameters such as state transition and measurement functions, and generate C/C++ code.