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Sensor Fusion, 6 credits
Sensorfusion, 6 hp
TSRT14
Main field of study
Electrical EngineeringCourse level
Second cycleCourse type
Programme courseExaminer
Gustaf HendebyDirector of studies or equivalent
Johan LöfbergEducation components
Preliminary scheduled hours: 41 hRecommended self-study hours: 119 h
ECV = Elective / Compulsory / Voluntary
Main field of study
Electrical EngineeringCourse level
Second cycleAdvancement level
A1XCourse offered for
- Computer Science and Engineering, M Sc in Engineering
- Industrial Engineering and Management - International, M Sc in Engineering
- Electronics Design Engineering, M Sc in Engineering
- Industrial Engineering and Management, M Sc in Engineering
- Applied Physics and Electrical Engineering, M Sc in Engineering
- Information Technology, M Sc in Engineering
- Applied Physics and Electrical Engineering - International, M Sc in Engineering
- Computer Science and Software Engineering, M Sc in Engineering
Entry requirements
Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshold requirements for progression within the programme, or corresponding.
Prerequisites
Digital Signal Processing, Signals and Systems.Intended learning outcomes
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.
Course 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.
Teaching and working methods
The course is organized in lectures/classes and laboratory work.
Examination
| UPG1 | Laboratory work | 3 credits | U, G |
| DAT1 | Computer examination | 3 credits | U, 3, 4, 5 |
Grades
Four-grade scale, LiU, U, 3, 4, 5Department
Institutionen för systemteknikDirector of Studies or equivalent
Johan LöfbergExaminer
Gustaf HendebyCourse website and other links
Education components
Preliminary scheduled hours: 41 hRecommended self-study hours: 119 h
Course literature
Teoribok från Studentlitteratur, Statistical Sensor Fusion| Code | Name | Scope | Grading scale |
|---|---|---|---|
| UPG1 | Laboratory work | 3 credits | U, G |
| DAT1 | Computer examination | 3 credits | U, 3, 4, 5 |
Regulations (apply to LiU in its entirety)
The university is a government agency whose operations are regulated by legislation and ordinances, which include the Higher Education Act and the Higher Education Ordinance. In addition to legislation and ordinances, operations are subject to several policy documents. The Linköping University rule book collects currently valid decisions of a regulatory nature taken by the university board, the vice-chancellor and faculty/department boards.
LiU’s rule book for education at first-cycle and second-cycle levels is available at http://styrdokument.liu.se/Regelsamling/Innehall/Utbildning_pa_grund-_och_avancerad_niva.
Course literature is preliminary.
Teoribok från Studentlitteratur, Statistical Sensor Fusion
Note: The course matrix might contain more information in Swedish.
I = Introduce, U = Teach, A = Utilize
| I | U | A | Modules | Comment | ||
|---|---|---|---|---|---|---|
| 1. DISCIPLINARY KNOWLEDGE AND REASONING | ||||||
| 1.1 Knowledge of underlying mathematics and science (G1X level) |
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X
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| 1.2 Fundamental engineering knowledge (G1X level) |
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X
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X
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| 1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level) |
X
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X
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| 1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level) |
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| 1.5 Insight into current research and development work |
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| 2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES | ||||||
| 2.1 Analytical reasoning and problem solving |
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X
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| 2.2 Experimentation, investigation, and knowledge discovery |
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X
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X
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| 2.3 System thinking |
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X
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X
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| 2.4 Attitudes, thought, and learning |
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X
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| 2.5 Ethics, equity, and other responsibilities |
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| 3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION | ||||||
| 3.1 Teamwork |
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X
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| 3.2 Communications |
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X
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| 3.3 Communication in foreign languages |
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| 4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT | ||||||
| 4.1 External, societal, and environmental context |
X
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| 4.2 Enterprise and business context |
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| 4.3 Conceiving, system engineering and management |
X
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X
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| 4.4 Designing |
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| 4.5 Implementing |
X
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X
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| 4.6 Operating |
X
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| 5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS | ||||||
| 5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development |
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| 5.2 Economic conditions for knowledge development |
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| 5.3 Identification of needs, structuring and planning of research or development projects |
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| 5.4 Execution of research or development projects |
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| 5.5 Presentation and evaluation of research or development projects |
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