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== Instructors ==
== Instructor ==
Dr. [[Abubakr Muhammad]], Assistant Professor of Electrical Engineering
Dr. [[Abubakr Muhammad]], Assistant Professor of Electrical Engineering

Revision as of 19:40, 3 January 2014

EE-662: Applied Paramter & State Estimation


Dr. Abubakr Muhammad, Assistant Professor of Electrical Engineering

Email: abubakr [at]

Office: Room 9-311A, 3rd Floor, SSE Bldg

Course Details

Year: 2012-13

Semester: Spring

Category: Graduate

Credits: 3

Elective course for electrical engineering majors

Course Website:

Course Description

In this course we develop a hands-on yet rigorous approach to tackling uncertainties in the dynamical evolution of an engineering system. We learn about the main sources of uncertainty and how to model them statistically. We learn that installing sensors on an uncertain system can help reduce this uncertainty. However, sensors themselves introduce noise. Still, there are amazingly efficient algorithms to process sensor data and minimize uncertainty due to both sensor and process noises. You will learn about the computer algorithm that navigated man to the moon and whose implementation requirements inspired the microelectronics revolution. Main topics of the course include Kalman filters, Bayesian estimation, Particle filters and Markov decision processes with lots of applications in robot navigation, geophysical data assimilation, signal detection, radar tracking, computer vision, aerospace guidance & control and many more.


  • To introduce an applied perspective on using estimation techniques in state space models of nonlinear non-Gaussian dynamical systems.
  • To introduce applications of state estimation in robot navigation, geophysical data assimilation, signal detection, radar tracking, computer vision etc.

Learning Outcomes

  • To identify and model uncertainties in sensors and dynamics of engineering systems.
  • To learn a unifying mathematical framework for tackling a vast range of estimation problems.
  • To appreciate common outcomes in attempts at uncertainty quantification from seemingly diverse disciplines of mathematical statistics, machine learning, signal processing, inverse problems and stochastic control theory.


EE-561. Digital Control Systems AND EE-501. Applied Probability OR By permission of instructor

Text book

The course will be taught from the following textbooks.

  • Optimal State Estimation by Dan Simon (Wiley, 2006)

Other important references include

  • Probabilistic Robotics by Thrun, Burgard, Fox (MIT Press, 2006)
  • Statistical Signal Processing (Part 1: Estimation theory) by Kay.
  • Estimation with Applications to Tracking and Navigation by Yaakov Bar-Shalom, X. Rong Li, Thiagalingam Kirubarajan (Wiley, 2001)

Grading Scheme

Home-works : 20%

Project: 25%

Midterm Examination: 25%

Final: 30%

Policies and Guidelines

  • Quizzes will be announced. There will be no makeup quiz.
  • Homework will be due at the beginning of the class on the due date. Late homework will not be accepted.
  • You are allowed to collaborate on homework. However, copying solutions is absolutely not permitted. Offenders will be reported for disciplinary action as per university rules.
  • Any appeals on grading of homeworks, quiz or midterm scores must be resolved within one week of the return of graded material.
  • Attendance is in lectures and tutorials strongly recommended but not mandatory. However, you are responsible for catching the announcements made in the class.
  • Many of the homeworks will include MATLAB based computer exercise. Some proficiency in programming numerical algorithms is essential for both the homework and project.


Week 1. Lecture 1.

Week 2. Lecture 2.

Lecture 3.

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