Understanding Kalman Filters, Part 1: Why Use Kalman Filters?

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. - Download examples and code - Design and Simulate Kalman Filter Algorithms: https://bit.ly/2Iq8Hks Design and use Kalman filters in MATLAB and Simulink: https://goo.gl/SVA9IK This 56-year-old algorithm is key to space travel, GPS, VR and more: https://goo.gl/l2WImv Watch other MATLAB Tech Talks: https://goo.gl/jD0uOH Get a free product trial: https://goo.gl/C2Y9A5 In the first example, you’re going to see how a Kalman filter can be used to estimate the state of a system (the internal temperature of a combustion chamber) from an indirect measurement (the external temperature of the combustion chamber). The second example demonstrates another common use of Kalman filters, in which you can optimally estimate the state of a system (e.g., the position of a car) by fusing measurements from multiple sources (e.g., an inertial measurement unit (IMU), an odometer, and a GPS receiver) in the presence of noisy measurements.