I wish to understand the difference between Kalman Filter and Recursive Least Squares since both of them use prediction and correction approach.

In Kalman filter, the value of existing state vector is updated based on the new information obtained from some exterior source.

Similarly, in recursive least squares as well, the value of the prediction is updated when the the new set of information is obtained from external sources.

So, how are they different from each other?


closed as off-topic by Crostul, user284331, Leucippus, HK Lee, Chris Custer May 10 '18 at 1:37

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  • $\begingroup$ You might be interested in reading Gilbert Strang's explanation of the Kalman filter in his book Introduction to Applied Mathematics. $\endgroup$ – littleO May 9 '18 at 19:15
  • $\begingroup$ Welcome to MSE. Please provide some context (for instance definitions of what you are talking about) and what you have found so far. $\endgroup$ – Bill O'Haran May 9 '18 at 19:22