EE 164

Stochastic and Adaptive Signal Processing

9 units (3-0-6)    |  third term
Prerequisites: ACM/EE/IDS 116 or equivalent.

Fundamentals of linear estimation theory are studied, with applications to stochastic and adaptive signal processing. Topics include deterministic and stochastic least-squares estimation, the innovations process, Wiener filtering and spectral factorization, state-space structure and Kalman filters, array and fast array algorithms, displacement structure and fast algorithms, robust estimation theory and LMS and RLS adaptive fields. Given in alternate years; offered 2022-23.

Instructor: Hassibi

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