Course Information HT, 2021


After completing the course, the student should have the ability to describe and implement the most important concepts, methods, and algorithms used for target tracking. More specifically, after the course the student should:

  • Understand and explain the fundamental principles of target tracking and target tracking systems.
  • Know of and be able to use common target and sensor models in target tracking.
  • Know how to deal with maneuvering targets, and sensor artifacts.
  • Be able to implement a single target tracker.
  • Understand the principles behind track management and be able to implement classic multi-target tracking methods.
  • Know of modern approaches to the multi-target tracking problem.

Course Responsible


The course assumes basic knowledge of probability theory and Bayesian estimation theory, e.g., as taught in the Sensor Fusion course (TSRT14). The computer exercises will require some coding in MATLAB or similar tool. Interested students with basic knowledge of math from an engineering MSc program are expected to be able to pick up what they need on the way.

More details can be found here.


Selected papers handed out during the course will be enough to follow the course.

For a fairly complete overview of the target tracking problem, methods, and algorithm collected at one place, the flowing books are good entry points.

  • S. Blackman and R. Popoli. Design and Analysis of Modern Tracking Systems. Archtech House, Norwood, MA. 1999.
  • Y. Bar-Shalom, P. K. Willett, and X. Tian. Tracking and Data Fusion: A Handbook of Algorithms. Yaakov Bar-Shalom Publishing. 2011.

Articles used throughout the course are listed here.


The course comprises 8 lectures. See the preliminary lecuter plan for content.


Credits for the course are awarded for three modules. You may choose which ones to attempt:

  • Short written exam covering the basic theory (2 ETCS credits)
  • Completed computer exercises (4 ETCS credits)
  • Completed project (3 ETCS credits)