• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

Upon successful completion of this course, students will be able to:

1. In this course, it is aimed to explain the positioning, target detection and tracking, central and distributed sensor fusion algorithms which have many applications in defense technologies. Simulation and laboratory studies with real data will also be done within the scope of the course.

   Contribution to Program Outcomes

   1. To gain in-depth knowledge about the sensor systems utilized in military applications.
   2. To analyze the sensor data and extract information from them.
   3. Find out new ways to improve current knowledge

   Method of assessment

   1. Homework assignment
   2. Term paper
2. Each student contributes to the development of writing ability by writing a report in a paper format within the scope of the project.

   Contribution to Program Outcomes

   1. To be able to express ideas and findings related to the research topic both orally and in writing.
   2. Demonstrating professional and ethical responsibility.

   Method of assessment

   1. Written exam
   2. Homework assignment
   3. Seminar/presentation
   4. Term paper

Contents
Week 1:	Basic definitions. Review of random variables and probability distributions.
Week 2:	Bayes' rule. Optimal estimators. Maximum a posteriori (MAP) and least squares error estimators.
Week 3:	State-space and measurement models. Markov processes.
Week 4:	Kalman filter. Derivation of the Kalman filter using a Bayesian approach.
Week 5:	Extended Kalman filter.
Week 6:	Particle filter.
Week 7:	Sensor fusion. Measurement models for Angle of Arrival (AoA), Time of Arrival (ToA), and Time Difference of Arrival (TDoA).
Week 8:	Tracking a target in clutter. Nearest neighbor, probabilistic data association, Gaussian sum filters.
Week 9:	Multi-target tracking with a known number of targets. Detection and track association.
Week 10:	Global nearest neighbor filter, joint probabilistic data association filter, multiple hypothesis tracking filter.
Week 11:	Underwater propagation problems and the application of filters.
Week 12:	Examples of different process models.
Week 13:	Examples of different observation models.
Week 14:	Detection and tracking of an unknown number of targets using random finite set theory.
Week 15*:	-
Week 16*:	-
Textbooks and materials:	1. B. Ristic, Beyond the Kalman Filter: Particle Filters for Tracking Applications, 2004. 2. J. V. Candy, Bayesian Signal Processing: Classical, Modern, and Particle Filtering Methods, 2016.
Recommended readings:	-
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:		25
Other in-term studies:		0
Project:		25
Homework:		20
Quiz:		0
Final exam:		30
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	16
Own studies outside class:	2	16
Practice, Recitation:	0	0
Homework:	5	16
Term project:	1	16
Term project presentation:	1	16
Quiz:	0	0
Own study for mid-term exam:	0	0
Mid-term:	0	0
Personal studies for final exam:	0	0
Final exam:	0	0
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)