TSRT14 · 6 credits · Advanced level (A1N) · Elektroteknik
Sensor Fusion
Active · next start VT 2027
41%
passed the latest regular sitting
DAT1 · 18 Aug 2026 · 17 students
50%
passed the regular sittings, all sittings over the last 5 years
DAT1 · 242 students
94%
Harder than 94% of courses at advanced level
Compared with 285 courses at the same level, ordinary exams over the last three academic years. 104 students.
Part of 9 programmes
- Master of Science in Computer Science and Engineering
- Master of Science in Electronics Design Engineering
- Master of Science in Information Technology
- Master of Science in Computer Science and Software Engineering
- Master of Science in Mechanical Engineering
- Master of Science in Engineering Mathematics
- Master of Science in Applied Physics and Electrical Engineering - International
- Master of Science in Applied Physics and Electrical Engineering
- Master's Programme in Data Science and Information Engineering
Other module
DAT1 Computer examination
- 5
- 4
- 3
- U
Main rounds
50% passed · 242 students
- 41%17
- 51%39
- 71%38
- 50%10
- 52%23
- 35%23
- 35%48
- 57%44
Later rounds
68% passed · 63 students
- 86%7
- 33%6
- 87%15
- 75%12
- 44%9
- 64%14
- Pass a later round: 68% (63 students), compared with 50% (242 students) on the ordinary exam
- Get 4 or 5: 17% (305 students)
Show small sittings (1)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 67%3
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
UPG1 Laboratory work
- G
Main rounds
100% passed · 174 students
- 100%37
- 100%34
- 100%27
- 100%31
- 100%45
Later rounds
100% passed · 21 students
- 100%7
- 100%14
- Pass a later round: 100% (21 students), compared with 100% (174 students) on the ordinary exam
Show small sittings (11)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%3
- 100%1
- 100%1
- 100%2
- 100%4
- 100%2
- 100%2
- 100%2
- 100%2
- 100%2
- 100%2
About the course
Aim
The student should after the course have the ability to describe the most important methods and algorithms for sensor fusion, and be able to apply these to sensor network, navigation and target tracking applications. More specifically, after the course the student should have the ability to:
- Explain and model the most common sensor and motion models used in sensor fusion applications.
- Apply and analyze algorithms for parameter estimation and detection in static signal models that include multiple sensors.
- Apply and analyze algorithms to solve state estimation in dynamic systems using multiple sensors.
- Identify cases where sensor fusion can be used to solve practical problems and design and evaluate solutions to address these problems.
Content
Fusion for linear and non-linear models. Sensor network localization and detection algorithms. Filter theory. The Kalman filter for sensor fusion. Extended and unscented Kalman filters. The particle filter. Simultaneous localization and mapping. Sensors and sensor-near signal processing. Motion models. Estimation and detection theory.
- Examiner
- Gustaf Hendeby
- Scheduled hours
- 41 h
- Main field of study
- Elektroteknik