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Automatic Control

Faculty of Engineering, LTH

Automatic Control

LTH best Master's thesis

Johanna Wilroth has been awarded the prize "LTHs Jubileumsstipendium 2020" for the best master thesis at LTH. In her thesis "Domain Adaptation for Attention Steering," Wilroth studied novel algorithms for improving the performance of hearing aids using EEG signals in combination with auditory input. The master thesis was done at the Department of Automatic Control, with Carolina Bergeling as main supervisor, in collaboration with the research center Eriksholm/Oticon in Denmark. The collaboration also involved the departments of Psychology and Mathematical Statistics at Lund University.

 

M.Sc by Sedin & Wadmark: Scene Aware Radar

M.Sc by Sedin & Wadmark

Seminarium

From: 2021-06-17 13:15 to 14:15
Place: https://lu-se.zoom.us/j/69789948666?pwd=QWtudFFTYWFsSFRCSlhaOTFHaXlmUT09
Contact: bjorn [dot] olofsson [at] control [dot] lth [dot] se
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Abstract:

In surveillance contexts, radars can be used to monitor an area, detecting and tracking moving objects inside it. Monitored areas in urban environments often contain many surfaces that reflect radar waves, which can have the undesired consequence of a single object producing multiple tracks due to multipath propagation effects. This thesis considers a method of identifying if a track is produced by a real object, or if it stems from multipath effects. The proposed method works by creating a machine-learning-based classifier and modelling the monitored scene over time. Tracks are assigned features based on their characteristics and the state of the scene model in regards to their position. These features are then used as inputs to the classifier model to produce the classification. We propose four machine learning-based classifier models, with two different sets of structures and features used. The classifier models are compared to a naive classifier model for reference. The proposed models all outperform the naive classifier, although some of them are biased. As for the usefulness of the scene model, the results are mixed but show promise. We believe that the scene model can improve classification performance further with more and better data.

Keywords: radar surveillance, multipath, machine learning, classification


Students:

    Anton Sedin

    David Wadmark

Advisors:

    Andreas Jakobsson, Division of Mathematical Statistics, LTH

    Björn Olofsson, Department of Automatic Control, LTH

    Sebastian Heunisch, Industrial supervisor

    Aras Papadelis, Industrial supervisor

Examiners:

    Rolf Johansson, Department of Automatic Control, LTH

    Johan Lindström, Division of Mathematical Statistics, LTH


 

The presentation will be held in Zoom: https://lu-se.zoom.us/j/69789948666?pwd=QWtudFFTYWFsSFRCSlhaOTFHaXlmUT09



Recent Publications

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In 2020, we moved to temporary offices at KC4 in Kemicentrum. Here we will stay until 2023 while the M-building is being renovated. See "contact" (in About) for the new visiting adress.

Formula Student

The department has a collaboration with the Lund Formula Student team, who are developing a fully autonomous race car to compete in Formula Student events all across Europe. They are always looking for interested and talented team members. If you are interested in a wide variety of technologies such as neural networks, control theory, ROS, etc, please visit their website or contact them at: technical.driverless@lundformulastudent.se.