Optimal State Estimator | Understanding Kalman Filters, Part 3

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.

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).

Check out additional resources:

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[cleansky]

you may also be interested in reading the paper "understanding the basis of the Kalman filter via a simple and intuitive derivation" by R. Faragher

[mehmetnuriozdemir]

This video made my day. Best Kalman Filter explanation from a Turkish Woman Scientist. Proud.

[AbdulSamad-hdsr]

simply short and detailed explanation .can realize the hard work behind this video.

[anilkumarsharma]

Osteoporosis testing 〰curves 〰 are example of the most important thing in practical experience and natural nature of the nature🌿🍃

[Aviation]

One of the best explanations I've seen. Good Job!

[droxid]

I have been waiting so long for this!
Thank you, I love this module and the lecturer's approach.

[pedropauloliborio]

Thanks for explain this in a graphical way. A lot of details, but now things makes more sense to me.

[andrewjewett]

Great visual explanation! (Humourous cartoons appreciated)

[Alhamdou_lilah]

hello i have a simple question please: why you didn't use the derivation of x instead of x(k) ?are they the same ?

[jumabekalikhanov]

u guyz r the beast. keep amazing us with ur excellence

[thanhcongai]

This video is very helpful. Thank you so much

[sathyanarayanankulasekaran]

Isn't it variance R, rather than Covariance R...for the Gaussian dist representing error

[susanius]

Why x_hat is distributed? Doesn't we suppose that the mathematical model for x_hat is deterministic?

[posthocprior]

In the previous video, the equations for the Kalman filter were transferred to an exponential distribution. That is, a distribution that doesn’t have to be normalized. In this video, pdfs are used and are multiplied together. This can only be done if the distributions are normalized. There is a large difference between a normalized pdf and an exponential distribution. With a pdf, you need enough data to find the true mean, in order to normalize the data. If you can’t, you can’t multiply the two distributions. So, this example wouldn’t work. My point, either the example of an exponential distribution should have carried to this video or it should have been mentioned which cases that this example can be used.

[miiirskiii]

Very nice! I love the example with the car.

[nicolenatsai]

I am a bit unclear on how the equations for x and y were derived, even carrying on from part 2. Please assist

[HamadaAlmasalma]

Really fantastic explanation :)
When Part 4 will be published?

[TheMechatronicEngineer]

R and Q are variances, not the covariances. Sigma is the standard deviation.

[RandomUser]

What's the difference between "Car dynamics" and "Car model"?

[RandomUser]

1:54 Why does the input u_k change from the throttle to the velocity just a few seconds later?