Multivariable Control: Correlated Noise

This lecture covers the generalization of the Kalman Filter to handle correlated noise, focusing on tuning noise variances and the key properties of uncorrelated noise. The instructor explains the steady-state Kalman predictor, the system verification process, and the performance evaluation of the Kalman Filter. Additionally, the lecture discusses parameter tuning in the Kalman Filter, innovation-based tests, and the significance of the innovation sequence in testing. Practical tricks and considerations for dealing with non-white noise and achieving optimal solutions are also addressed.