Sensor fusion
Lund, May, 2011


First three lectures will be in M:E (Bottom floor north part of the M-building in Lund).
Slides for first part can be downloaded here
Exercises can be downloaded here (password protected)
Signal and Systems Lab can be downloaded here (password protected)


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.

The course consists of

Lectures: 5 (13 hours)

Exercises: ?

Laboratory exercise: 1


Statistical Sensor Fusion. Studentlitteratur, 2010.


Written examination with Matlab.



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

Preliminary lecture plan

1 slides 1 comp


Slides will be linked from the lecture number in advance.
1, May 3, 13-16 Estimation theory for linear and nonlinear models.
2, May 4, 9-12 Sensor network applications and detection theory
3, May 5, 9-12 Nonlinear filter theory. Standard, extended and unscented Kalman filters
4, May 16, 13-15 The point-mass filter and the particle filter.
5, May 17, 9-11 Particle filter theory, including the marginalized particle filter.