Lectures
Time: M and W 11:00am - 12:15pm
Location: GESB131
Problem sessions Time:
F 9:00am - 10:15am Location: 320-109
Reading 4 lecture sets to be handed out throughout the quarter.
Prerequisites EE278: Introduction to Statistical Signal Processing
Course requirements
Weekly homework Assigned each Wednesday and due the following Wednesday before 5PM.
You can work on the
homework in small groups, but you must write your own homework to hand
in.
Midterm examination Date and location: Monday, May 11,
11:00am - 12:15pm, in class.
Open class handouts and notes.
Final examination Date and location: Tuesday, June 9,
8:30am - 11:30am, location TBA.
Open class handouts and notes.
Grading
Homework: 30%
Midterm examination: 30%
Final examination: 40%
Catalog description
Random signals in electrical engineering. Discrete-time random processes: stationarity and ergodicity, covariance sequences, power spectral density, parametric models for stationary processes. Fundamentals of linear estimation: minimum mean squared error estimation, optimum linear estimation, orthogonality principle, the Wold decomposition. Causal linear estimation of stationary processes: the causal Wiener filter, Kalman filtering. Parameter estimation: criteria of goodness of estimators, Fisher information, Cramer-Rao inequality, Chapman-Robbins inequality, maximum likelihood estimation, method of moments, consistency, efficiency. ARMA parameter estimation: Yule-Walker equations, Levinson-Durbin algorithm, least squares estimation, moving average parameter estimation, modified Yule-Walker method for model order selection. Spectrum estimation: sample covariances, covariance estimation, Bartlett formula, periodogram, periodogram averaging, windowed periodograms.