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?