Sensor Fusion (TSRT14)
Course Information VT2, 2024
Goal:
A student taking this course, should after completing 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 nonlinear 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 nonlinear filters (extended Kalman filter, unscented Kalman filter, particle filter) to nonlinear 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.
Look here and in “studieinfo” for a more detailed description of the course content.
The course comprise:
| Lectures | 10 |
| Exercise sessions | 8 |
| Laboratory exercises | 2 |
Lectures and Exercise Sessions
Preliminary lecture and exercise plan.
Labs
For questions regarding the labs, the schedule and sign-up, contact the course assistant Chuan Huang (chuan.huang@liu.se).
Lab 1: Localization in acoustic sensor networks
For details about the lab, follow the link above. Sign up for the data collection in Lisam, and hand in the report work in Lisam.
| Step | Deadline |
|---|---|
| Report v 1 | Monday April 29, 2024, at 12:00 |
| Review | Monday May 6, 2024, at 12:00 |
| Report v 2 | Monday May 13, 2024, at 12:00 |
| Feedback provided | Monday May 20 2024, at 12:00 |
| Report v 3 (if needed) | Friday June 7, 2024, at 12:00 |
Lab 2: Orientation estimation using smartphone sensors
For details about the lab, follow the link above. Sign up for the introductory session, and the reporting session in Lisam.
Examination
Written examination with Matlab. Examples of previous exams.
Course Material
Literature
- Statistical Sensor Fusion. Fredrik Gustafsson. Studentlitteratur, 2018, third edition.
- Statistical Sensor Fusion - Exercises. Christian Lundquist, Zoran Sjanic, and Fredrik Gustafsson. Studentlitteratur, 2015.
- Statistical Sensor Fusion - Laborations:
Toolbox
Use the links below to download the Signals and Systems toolbox that is used in the course:
To activate the toolbox, run the included command
initSigSys
in matlab. To use the latest version of the toolbox in the Linux computer labs, run
module add courses/TSRT14
in a terminal prior to opening matlab, or install a current version of the toolbox in your home directory as you would at home.
Organizers
Lecturer and examiner:
Teaching assistant: