Estimation and Identifiation in Dynamical Systems (048825)
Nahum Shimkin, Spring 2009
Handouts:
Syllabus
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)
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)
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
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