Lund, May, 2011

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)

- 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

- 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. Data files: Calibration and test drive

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

Nr. | Content |
---|---|

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. |