Markus Gardill is professor for Satellite Communication Systems at the chair of computer science VII - robotics and telematics at the university of Würzburg. He received the Dipl.-Ing. and Dr.-Ing. degree in systems of information and multimedia technology/electrical engineering from the Friedrich-Alexander-University Erlangen-Nürnberg, Germany, in 2010 and 2015, respectively, where he was a research assistant, teaching fellow, and later head of the team for radio communication technology.
Between 2015 and 2020 he was R&D engineer and research cluster owner for optical and imaging metrology systems at Robert Bosch GmbH. Later he joined InnoSenT GmbH as head of the group radar signal processing & tracking, developing together with his team new generations of automotive radar sensors for advanced driver assistance systems and autnomous driving.
His main research interest include radar and communication systems, antenna (array) design, and signal processing algorithms. His particular interest is space-time processing such as e.g. beamforming and direction-of-arrival estimation, together with cognitive and adaptive systems. He has a special focus on combining the domains of signal processing and microwave/electromagnetics to develop new approaches on antenna array implementation and array signal processing. His further research activities include distributed coherent/non-coherent networks for advanced detection and perception, machine-learning techniques for spatial signal processing, highly-flexible software defined radio/radar systems, and communication systems for NewSpace.
Markus Gardill is member of the IEEE Microwave Theory and Techniques Society (IEEE MTT-S). He served as co-chair of the IEEE MTT-S Technical Committee Digital Signal Processing (MTT-9), regularly acts as reviewer and TPRC member for several journals and conferences, and currently serves as associate editor of the Transactions on Microwave Theory and Techniques. He is a Distinguished Microwave Lecturer (DML) for the DML term 2018-2020 with a presentation on signal processing and system aspects of automotive radar systems.
PhD in Electronics and Information Technology, 2015
Dipl.-Ing. (equiv. M.Sc.) in Electronics and Information Technology, 2010
A multi-layer perceptron for indicating the number of targets present in a range-velocity cell of automotive radar sensors is examined and compared with a state-of-the-art approach based on a Generalized Likelihood Ratio Test. The multi-target indication is typically used for direction-of-arrival es- timation to decide whether resolution in the angular domain is necessary. We focus on the practically relevant challenge of deciding between a single-target and a two-target scenario. Compared to the state-of-the-art approach which requires a preceding maximum likelihood DoA estimate and a precise array model, the proposed multi-layer perceptron directly operates on the single-snapshot spatial covariance matrix estimate. The array model inherently is learned by the network during the training process. The evaluation of the MLP in terms of classification accuracy shows that a performance similar to the Generalized Likelihood Ratio Test is achieved.