Bachelor Thesis / Master Thesis – Interference Detection for TDoA Localization Systems using Machine Learning Methods
The Fraunhofer-Gesellschaft (www.fraunhofer.com) currently operates 76 institutes and research institutions throughout Germany and is the world’s leading applied research organization. Around 30 000 employees work with an annual research budget of 2.9 billion euros.
The Fraunhofer IIS in cooperation with the TU Ilmenau set up a testbed to enable the localization of mobile end-points using the LPWAN standard mioty for application in current research topics like IoT. The localization is based on time difference of arrival measurements (TDoA).
Several effects in the transmission channel, like phase noise, multipath effects, or interferences from different communication systems in the same frequency band, lead to a decreasing performance of the localization accuracy. The subject of this thesis is the detection and classification of interference from communication systems within the same frequency band to improve localization accuracy. Therefore, suitable features must be extracted from the received baseband signals, and a model must be trained to detect and classify the interference patterns. To validate the developed classification model, simulations must be conducted. These simulations shall include different types of interference (e.g., narrowband, wideband, chirps, etc.). The trained model must be tested in a real-world environment using the described testbed.
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