TTK4250 - Sensor Fusion


Examination arrangement

Examination arrangement: Portfolio assessment
Grade: Letters

Evaluation form Weighting Duration Examination aids Grade deviation
Home examination 25/100 2 hours
work 75/100

Course content

Examples of sensor fusion. Random variables and probability distributions. Concepts in estimation: ML, MAP, MMSE estimator, total probability theorem, Bayes and orthogonality principle. The multivariate Gaussian and the product identity. The Kalman filter. Stochastic processes driven by white noise. The extended Kalman filter. Particle filters. Gaussian mixtures. Hybrid systems and the IMM algorithm. Data association in single and multiple target tracking. The PDAF and the JPDA. Methods for detection and track initiation. Smoothing. Multiple hypothesis tracking, integer programming and PMBM. Loosely coupled inertial navigation systems (INS). Error models for INS. The multiplicative extended Kalman filter for attitude estimation. Nonlinear INS methods. The standard model for feature-based simultaneous localization and mapping (SLAM) and its EKF solution. Data association for SLAM. Rao-Blackwellization and FastSLAM. The information filter. Graphical SLAM methods. Practical implementation of methods for multi-target tracking, INS and SLAM.

Learning outcome

Knowledge: Knowledge about core applications in sensor fusion. Knowledge about key results in probability and estimation. Knowledge about linear and nonlinear filtering techniques. Knowledge about methods for target tracking in clutter. Knowledge about modeling and implementation of INS. Knowledge about feature-based SLAM methods. Skills: Be able to use different estimation principles in a variety of estimation problems. Manipulate multivariate Gaussians. Design and implement Kalman filters for Gaussian linear filtering problems. Analyze the stochastic properties of Gaussian-linear filtering. Implement particle filters and/or nonlinear extensions of the Kalman filter for non-linear filtering problems. Implement single-target and multi-target tracking methods. Implement methods for feature-based SLAM. General competence: Be able to apply the fundamental estimation principles in cooperation with other disciplines. Consciousness about the role of sensor fusion in automation. Consciousness about the strengths and weaknesses of different sensor fusion methods.

Learning methods and activities

The course is given as a mixture of lectures, written assignments and computer assignments. The computer assignments will involve implementation of sensor fusion methods on real data. A minimum of 3 written assignments and 3 computer assignments must be approved to enter the final exam.

Compulsory assignments

  • Exercises

Further on evaluation

Portfolio evaluation ("Mappevudering") is used to define the final grade in the subject. Parts of the portfolio are the final exam in writing 55%, and based on the computer assignments 45%. The result of each part is given in percentage units, while evaluation of the entire portfolio (the final grade) is given as a letter. The exams are only given in English. Students are free to choose Norwegian or English for written assessments. If there is a re-sit examination, the examination form may change from written to oral. All 3 computer assignments (45%) need to be retaken in addition to the main exam (55%).

Specific conditions

Exam registration requires that class registration is approved in the same semester. Compulsory activities from previous semester may be approved by the department.

Required previous knowledge

At least one of the following (or equivalent from other universities): TTK4115 Linear system theory, TTT4275 Estimation, detection and classification, TMA4268 statistical learning or TMA4267 Linear statistical models.

Course materials

Information on this is given at the start of the semester.

More on the course



Version: 1
Credits:  7.5 SP
Study level: Second degree level


Term no.: 1
Teaching semester:  AUTUMN 2020

No.of lecture hours: 3
Lab hours: 3
No.of specialization hours: 6

Language of instruction: English, Norwegian

Location: Trondheim

Subject area(s)
  • Engineering Cybernetics
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Engineering Cybernetics



Examination arrangement: Portfolio assessment

Term Status code Evaluation form Weighting Examination aids Date Time Digital exam Room *
Autumn ORD Home examination (1) 25/100

Release 2020-11-24

Submission 2020-11-24

Release 09:00

Submission 11:00

Room Building Number of candidates
Summer UTS Home examination 25/100 INSPERA
Room Building Number of candidates
Autumn ORD work 75/100 INSPERA
Room Building Number of candidates
Summer UTS work 75/100 INSPERA
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
  • 1) Merk at eksamensform er endret som et smittevernstiltak i den pågående koronasituasjonen. Please note that the exam form has changed as a preventive measure in the ongoing corona situation.

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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