Course DescriptionThis course is going to be about basic target tracking theory and techniques including
- Track initiation, maintanence and deletion
- Maneuvering target tracking
- Multiple model filtering
- Interacting multiple model (IMM) filter
- Multi target tracking
- Global nearest neighbors (GNN)
- (Joint) probabilistic data association (JPDA)
- Multiple hypothesis tracking (MHT)
- Multi sensor target tracking
- Multi sensor architectures
- Random set based approaches (PHD, CPHD)
- Probabilistic multiple hypothesis tracking (PMHT)
- Extended target tracking (ETT)
PrerequisitesThe engineering probability theory is essential. We are going to use (extended) Kalman filters (or alternative Bayesian filters) as sub-blocks in the algorithms. Therefore, the sensor fusion course or basic Kalman filter knowledge is necessary. For the computer exercises, a fair knowledge of Matlab is required.
LiteratureThere 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.
- S. Blackman and R. Popoli, Design and Analysis of Modern Tracking Systems, Artech House, Norwood MA, 1999.
OrganizationThe course is going to be composed of lectures and related computer exercises. There is no written exam for the course and instead students will submit the reports of their their solutions to the exercises.
LecturesThe standard two hour lectures will more or less follow the topic order given above. The program is given in the lectures page.
Computer ExercisesThe course is going to include extensive computer exercises which will involve the implementation of the algorithms covered in class on simplified examples.
Project (optional)For people who would like to do more and get more points (3hp), there is an opportunity to do a project which can take place after the course is finished.
Contact PersonEmre Ozkan (tel: 013-281338, email: emre_at_isy.liu.se).
Informationsansvarig: EMRE OZKAN
Senast uppdaterad: 2022-06-23