Course Information

Sensor fusion, TSRT14
VT2, 2012

News:

There is a new edition of the course book, preliminary available in week 11. This is mainly the first edition updated with the errata list.

Goal:

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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The course consists of

Lectures: 10

Exercises: 8

Laboratory exercises: 2

• Localization in acoustic sensor networks. A moving target is transmitting short acoustic signals and a network with microphones detects, localizes and tracks the target. Matlab files are provided. The lab report is due on Wednesday April 25, 12:00 pm, and the review report is due on Thursday May 3, 17:00 pm. The reports may be written in Swedish or English.
• Simultaneous localization and mapping in acoustic sensor networks. A network with loudspeakers sends out their unique acoustic signature, and a moving platform collects these signatures, builds a map of the loudspeakers and uses this map to localize itself. The lab report is due on Friday May 18, 12:00 pm. The data sets to be used are provided. Also the raw data is provided (however not needed to accomplish the lab).

Both these labs contain one experimental phase in our lab (RT3) and one off-line phase where algorithms will be developed, and applied to the data. The result will be two reports. Suitable group size is two students and you have to register for both labs, which you can do online in due time, by following the link here

The labs are conducted in the student lab of the Division of Automatic Control, Laboteket, which is located in house B (entrance 27, corridor C).

Laboteket is available also outside the scheduled time. In order to enter the Laboteket you will need your LiU-card.

The course assistant is responsible for the lab schedule Niklas Wahlström , nikwa_at_isy.liu.se, 282803.

The lab report (.pdf file) should be sent as an attachment in an e-mail to the course assistant, also CC Urkund (e-mail: nikwa61.liu@analys.urkund.se) in this e-mail.

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Email list

Information during the course will be sent to the email list tsrt14-vt2012@student.liu.se. Information about the email list can be found on "Studentsidan".

Literature

Statistical Sensor Fusion. Studentlitteratur, 2012, Second Edition.
Statistical Sensor Fusion - Exercises. LiU.
Statistical Sensor Fusion - Laborations. Available from homepage.

Examination

Written examination with Matlab.

Organizers

Lecturers:

Fredrik Gustafsson , e-mail: fredrik_at_isy.liu.se.

Emre Özkan , e-mail: emre_at_isy.liu.se.

Course assistents:

Niklas Wahlström , e-mail: nikwa_at_isy.liu.se.

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Preliminary lecture plan

Lectures

Slides will be linked from the lecture number in advance.

Nr.	Content
1 slides	Course overview. Estimation theory for linear models.
2 slides	Estimation theory for nonlinear models with sensor network applications.
3 slides	Detection theory with sensor network applications.
4 slides	Nonlinear filter theory. The Kalman filter. Filter banks.
5 slides	Kalman filter approximation for nonlinear models (EKF, UKF).
6 slides	The point-mass filter and the particle filter.
7 slides	The particle filter theory. The marginalized particle filter.
8 slides	Simultaneous localization and mapping (SLAM).
9 slides	Modeling and motion models.
10 slides	Sensors and sensor-near signal processing.

Preliminary exercise plan

Lessons

Nr.	Ex	Content
1	2.1, 2.4, 3.1, 3.13, 3.5b, 2.3, 2.5	Estimation.
2	4.10, 4.2, 4.3, 5.1, 16.1	Sensor networks.
3	2.10, 3.9, 3.6, 4.7, 4.8, 4.9	Computer-based estimation and detection.
4	6.1, 6.2, 7.1, 7.2, 7.3	Optimal filtering.
5	8.1, 8.2, 8.4, 9.1, 9.2, 9.3	Approximative filtering.
6	8.6, 9.5, 7.10, (16.3)	Computer-based filtering.
7	11.1, 11.3	Computer-based SLAM.
8	12.1, 12.3, 13.1, 13.2, 14.2, 12.2	Modeling and motion models.