Estimation and Identifiation in Dynamical Systems (048825)

Nahum Shimkin, Spring 2009



Lecture Notes (pdf):

Chapter 1: Introduction

Chapter 2: Statistical Estimation

Chapter 3: The Wiener Filter

Chapter 4: Derivations of the Discrete-Time Kalman Filter 

Chapter 5: The Continuous-Time Kalman Filter

Chapter 6: The Steady-State Filter

Chapter 7: Optimal Smoothing  [Not included in course material]

Chapter 8: Kinematic Models for Target Tracking  [Self Reading]

Chapter 9: Multi-Model State Estimation

Chapter 10: Nonlinear Filters

Chapter 10a: Particle Filters [Powerpoint]

Chapter 11: Hidden Markov Models (HMMs)


Homework (can be submitted in pairs)

Home Assignment 1:  due on April 7

Home Assignment 2: due on May 5

Home Assignment 3 (computer): due on May 19

Home Assignment 4: due 26/5

Home Assignment 5: due 23/6

Home Assignment 6 : due 28/7 (not mandatory)


Final Presentations and Exam:


          Guidelines for final presentations 

          Presentation Schedule (all are in room 351)


          The exam will take place on July 28, 16:30-19:30, room 351.



Reference Material

v     R. E. Kalman, A New Approach to Linear Filtering and Prediction Problems,
Trans. of the ASME–Journal of Basic Engineering, 82 (Series D): 35-45, 1960.



Web Pointers

v     The Kalman Filter: A very useful reference site with many pointers.