Prerequisites: EE 235 or equivalent
Description: Covers autoregressive and mov- ing-average models, state estimation and parameter identification (including least square and maximum likelihood formulations), observability theory, synthesis of optimum inputs, Kalman-prediction (filtering and smoothing), steady-state and frequency domain analy- sis, on-line estimation, colored noise, and nonlinear filtering algorithms.