Understanding Kalman Filters, Part 3: Optimal State Estimator

Watch this video for an explanation of how Kalman filters work. Kalman filters combine two sources of information, the predicted states and noisy measurements, to produce optimal, unbiased estimates. Download examples and code - Design and Simulate Kalman Filter Algorithms: https://bit.ly/2Iq8Hks Download code: http://bit.ly/2QbbFOt Watch other MATLAB Tech Talks: https://goo.gl/jD0uOH Get a free product trial: https://goo.gl/C2Y9A5 More Kalman Filter Resources: https://goo.gl/4Qsqg4 https://goo.gl/dgXfrS The example introduces a linear single-state system where the measured output is the same as the state (the car’s position). The video explains process and measurement noise that affect the system. You’ll learn that the Kalman filter calculates an unbiased state estimate with minimum variance in the presence of uncertain measurements. The video shows the working principles behind Kalman filters by illustrating probability density functions. You can create the probability density functions discussed in the video using the MATLAB script provided in the Controls Tech Talks repository (please see the link above).