Course Number:

Credit Hours:

4

Course Title:

Statistical and Adaptive Signal Processing

Course Description:

Uses linear mean square estimation concepts as a starting point to explore some important areas of statistical and adaptive signal processing. Offers the student an opportunity to gain a thorough understanding and a working knowledge of FIR and IIR Wiener filtering, linear prediction and autoregressive model matching, the Levinson and Schur algorithms and the lattice filter configuration, autocorrelation estimation and the deterministic least squares method, LMS and RLS adaptive filters, and order recursive (triangular and lattice) architectures. Offers an opportunity to acquire a firm grasp of the Kalman filter; spectral and covariance factorization; performance analysis of adaptive filters under nonstationary conditions; and (time permitting) a factual knowledge of some basic concepts concerning fundamentals of spectrum estimation, nonstationary spectrum analysis, IIR (Laguerre-based) lattice configuration, and nonlinear adaptive filtering.