Jona Eisele, M.Eng.

Industrial PhD

Ultrasonic environmental sensing based on deep learning methods

There is a growing need in advanced environmental sensing for autonomous driving applications. Improving the performance of ultrasonic sensors is of great interest because of their robustness, low production costs and widespread use. Today’s sensors use the pulse-echo method to calculate the distance to obstacles. This work examines methods of signal processing and feature extraction as well as the use of machine learning to perform ultrasound-based classification of objects.

Research Topics

Ultrasound
Machine Learning
Deep Learning

Project Partners

Technical University of Munich
Stuttgart Media University
Robert Bosch GmbH

2023

Eisele, Jona; Gerlach, André; Maeder, Marcus; Koch, Andreas; Marburg, Steffen: Objektklassifikation auf Basis von Luftultraschall für Aufgaben der Umfeldsensierung mittels Convolutional Neural Network. Fortschritte der Akustik - DAGA 2023, 49. Jahrestagung für Akustik, 06.-09. März 2023, Hamburg, Deutsche Gesellschaft für Akustik e.V. (DEGA), 2023https://pub.dega-akustik.de/DAGA_2023 more… BibTeX
Eisele, Jona; Gerlach, André; Maeder, Marcus; Marburg, Steffen: Convolutional neural network with data augmentation for object classification in automotive ultrasonic sensing. The Journal of the Acoustical Society of America 153 (4), 2023, 2447-2459 more… BibTeX Full text ( DOI )

2022

Eisele, Jona; Gerlach, André: Künstliche Intelligenz für akustische Sensorsysteme. Fortschritte der Akustik - DAGA 2022, 48. Jahrestagung für Akustik, 21.-24. März 2022, Stuttgart, Deutsche Gesellschaft für Akustik e.V. (DEGA), 2022https://www.dega-akustik.de/publikationen/online-proceedings/ more… BibTeX