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Sensor Fusion, 6 ECTS credits.
/Sensorfusion/
For:
D
ED
I
Ii
IT
Y
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Prel. scheduled
hours: 41
Rec. self-study hours: 119
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Area of Education: Technology
Main field of studies: Electrical Engineering
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Advancement level
(G1, G2, A): A
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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
- Understand the fundamental principles in estimation and detection theory.
- Implement algorithms for parameter estimation in linear and non-linear models.
- Implement algorithms for detection and estimation of the position of a target in a sensor network.
- Apply the Kalman filter to linear state space models with a multitude of sensors.
- Apply non-linear filters (extended Kalman filter, unscented Kalman filter, particle filter) to non-linear or non-Gaussian state space models.
- Implement basic algorithms for simultaneous localization and mapping (SLAM).
- Describe and model the most common sensors used in sensor fusion applications.
- Implement the most common motion models in target tracking and navigation applications.
- Understand the interplay of the above in a few concrete real applications.
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Prerequisites: (valid for students admitted to programmes within which the course is offered)
Digital Signal Processing, Signals and Systems.
Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshhold requirements for progression within the programme, or corresponding.
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Organisation:
The course is organized in lectures/classes and laboratory work.
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Course contents:
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.
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Course literature:
Theory book published by Studentlitteratur, Statistical Sensor Fusion
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Examination: |
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Computer examination
Laboratory work
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3 ECTS
3 ECTS
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Course language is Swedish.
Department offering the course: ISY.
Director of Studies: Johan Löfberg
Examiner: Gustaf Hendeby
Link to the course homepage at the department
Course Syllabus in Swedish
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