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THE INFORMATION ABOUT EE793 ON THIS PAGE BELONGS TO THE TIME WHEN THE COURSE WAS FIRST GIVEN IN SPRING 2013 AND IS NOW OBSOLETE. THE PROSPECTIVE AND CURRENT STUDENTS MUST FOLLOW THE UP-TO-DATE INFORMATION AND INSTRUCTIONS OF THE CURRENT INSTRUCTOR OFFERING EE793.

This is an advanced graduate level course given for the first time in Department of Electrical & Electronics Engineering of Middle East Technical University. The course will be an extended version of the course designed and given by Umut Orguner in Division of Automatic Control at the Department of Electrical Engineering of Linköping University (LiU), Linköping, Sweden. Follow this link to reach the old course web-page.

Course Description

Target tracking, in general, is the theory and practice of using the state estimation tools which are developed theoretically under strict assumptions, in a real environment where those assumptions are commonly violated. Target tracking is used extensively in

  • Both commercial (air traffic control) and defence radar systems
  • Video surveillance systems
  • Robotics

This course is about basic target tracking theory and related techniques including

  • Kalman filtering
  • Track initiation, maintanence and deletion (Single target tracking)
  • Measurement to Track Association (Single target tracking)
    • Nearest neighbors (NN)
    • Probabilistic data association (PDA)
  • Maneuvering target tracking
    • Classical approaches: Adjustable level process noise, input estimation, variable state dimension
    • Multiple model filtering
    • Interacting multiple model (IMM) filter
  • Multiple target tracking
    • Global nearest neighbors (GNN)
    • Joint probabilistic data association (JPDA)
    • Multiple hypothesis tracking (MHT)
  • Multi sensor target tracking
    • Multi sensor architectures
    • Track association and fusion
    • Out of sequence measurements
In addition to these, the last lectures of the course will include an overwiew of some unconventional methods that have gained popularity during the last years in the tracking community. These might include
  • Extended target tracking
  • Random set based approaches (PHD, CPHD filters)
  • Probabilistic multiple hypothesis tracking (PMHT)
  • Track before detect (TBD)

Prerequisites

  • Engineering level probability theory and stochastic processes are essential and will not be covered in class.
  • We are going to use (extended) Kalman filters (or alternative Bayesian filters) as subblocks in the algorithms. Therefore, basic Kalman filter knowledge is highly beneficial. At the beginning of the course, the related background about Kalman filters will be covered.
  • For the computer exercises, a fair knowledge of Matlab is required.

Literature

There is unfortunately no single book suitable for all of our purposes. The following book is now a famous one that covers the most of the material relavant to the course ahnd hence will be the main textbook.
  • S. Blackman and R. Popoli, Design and Analysis of Modern Tracking Systems, Artech House, Norwood MA, 1999.

When we need deeper information about the covered subjects, the additional material will be distributed in the class.

Other books on target tracking that might be of use are:

  • Y. Bar-Shalom, P. K. Willett and X. Tian, Tracking and Data Fusion: A Handbook of Algorithms, YBS Publishing, 2011.
  • S. Challa, M. R. Morelande, D. Musicki and R. J. Evans, Fundamentals of Object Tracking, Cambridge University Press, 2011.
  • M. Mallick, V. Krishnamurthy and B.-N. Vo (Editors), Integrated Tracking, Classification, and Sensor Management: Theory and Applications, John Wiley & Sons, 2012.
  • R. P. S. Mahler, Statistical Multisource-Multitarget Information Fusion, Artech House, 2007.
  • B. Ristic, S. Arulampalam and N. Gordon, Beyond the Kalman Filter: Particle Filters for Tracking Applications, Artech House, 2004.
  • Y. Bar-Shalom, X. R. Li, and T. Kirubarajan, Estimation with Applications to Tracking and Navigation. Wiley, 2001.
  • Y. Bar-Shalom and X. R. Li, Multitarget-Multisensor Tracking: Principles, Techniques. Storrs, YBS Publishing, 1995.

Organization

The course is going to be composed of lectures with related computer exercises.

Lectures

The standard three hour lectures will more or less follow the topic order given above. A more detailed but still tentative program is given in the lectures page.

Computer Exercises

The course is going to involve extensive computer exercises which will involve the implementation of the algorithms covered in class on simplified examples. A tentative plan of the exercises is given in the exercises page.

Grading

  • Midterm: 25% ,
  • Final Exam: 25%,
  • Computer Assignments: 50%.