6.433 Recursive Estimation

6.435 System Identification

Spring 2002

Sanjoy Mitter, Lecturer

Texts | Problem Sets | Handouts | Course Description

CHANGE OF LOCATION & TIMES FOR REMAINDER OF SEMESTER

CLASS WILL BE HELD IN 36-144

Monday, 2:00 - 4:00 (NOTE NEW START TIME)

Wednesday 2:00 - 4:00 (NOTE NEW START TIME)

LECTURES:

April 1: Least Squares Smoothing (Chapter 10)

April 3, 8 and 10: Canonical Spectral Factorization, Wiener Theory for Scalar Processes, Recursive Wiener Filtering, System Theory Approach to Rational Spectral Factorization (Chapters 6,7,8); Asymptotic Behavior of Kalman Filters (Chapter 14)

MAY NEED ADDITIONAL LECTURE

Please note that ALL lectures are 2 hours

Lecture 1: Summary of Course

Confluence of Ideas in Communications, Signal Processing,Recursive Estimation and Identification

Chapter 1 of book

Lectures 2-5:

Deterministic and Stochastic Least Squares

Problems: Chapters 2 and 3 of book

Static Parameter Estimation: Notes.

Lectures 6-9:

Chapters 4, 5 and 6 of book

Innovations Process, State Space Models

Discrete-time situation only

Lectures 10 and 11:

Wiener Filter: Chapter 7 of book

Lectures 12-17:

Recursive Wiener Filters, Kalman Filters, Smoothed Estimators:

Chapters 8-10 of book