
General
EE 503 Statistical Signal Processing and Modeling
(Fall 2020– 2021)Short Description:
This course is the first course on statistical signal processing in the graduate curriculum of Department of Electrical and Electronics Engineering, Middle East Technical University (METU). Topics covered in this course are random vectors, random processes, stationary random processes, wide sense stationary processes and their processing with LTI systems with applications in optimal filtering, smoothing and prediction. A major goal is to introduce the concept of mean square error (MSE) optimal processing of random signals by LTI systems.
For the processing of the random signals, it is assumed that some statistical information about the signal of interest and distortion is known. By utilizing this information, MSE optimal LTI filters (Wiener filters) are designed. This forms the processing part of the course. The estimation of the statistical information to construct Wiener filters forms the modeling part of the course. In the modeling part, we examine AR, MA, ARMA models for random signals and give a brief discussion of Pade, Prony methods for the deterministic modeling. Among other topics of importance are decorrelating transforms (whitening), spectral factorization, KarhunenLoeve transformInstructor: Cagatay Candan
youtubeplaylist for all 47 EE 503 lecture videos: