Comparison of a noisy image of an astronaut (above) with the image after a Wiener filter is applied (below). Original image courtesy of NASA and is in the public domain. Noisy and filtered images courtesy of OCW.
Instructor(s)
Prof. George Verghese
Prof. Alan V. Oppenheim
Prof. Peter Hagelstein
MIT Course Number
6.011
As Taught In
Spring 2018
Level
Undergraduate
Course Description
Course Features
Course Description
This course covers signals, systems and inference in communication, control and signal processing. Topics include input-output and state-space models of linear systems driven by deterministic and random signals; time- and transform-domain representations in discrete and continuous time; and group delay. State feedback and observers. Probabilistic models; stochastic processes, correlation functions, power spectra, spectral factorization. Least-mean square error estimation; Wiener filtering. Hypothesis testing; detection; matched filters.
Other Versions
Other OCW Versions
OCW has published multiple versions of this subject. 
Archived versions: 