Mitarbeiter

M. Sc. Jonas Fuchs

Kontakt

  • E-Mail:
  • Telefon: 09131/85-27190
  • Fax: 09131/85-28730
  • Raum: 01.178 C
  • Wetterkreuz 15
    91058 Erlangen

Über Jonas Fuchs

Lebenslauf

Jonas Fuchs schloss sein Bachelorstudium im Fach Elektrotechnik-Elektronik-Informationstechnik (EEI) an der Friedrich-Alexander-Universität Erlangen-Nürnberg im April 2016 ab. Das anschließende Masterstudium im Fach EEI mit dem Schwerpunkt Informationstechnik beendete er im Februar 2018 erfolgreich. Seit Juni 2018 ist er als wissenschaftlicher Mitarbeiter im Team Circuits, Systems und Hardware Test (CST) am Lehrstuhl für Technische Elektronik beschäftigt.

Arbeitsgebiete

  • Digitale Radar-Signalverarbeitung und Machine-Learning Algorithmen

Abschlussarbeiten

Abschlussarbeiten StudOn

MA Objekt Tracking mittels Maschinellem Lernen basierend auf Radar Daten intern
MA Objekt Detektion mittels Maschinellem Lernen basierend auf Radar Daten intern
MA Fusion von inkohärenten Radardaten zur Winkelschätzung mit Neuronalen Netzen extern
MA Detektion und Klassifikation von Verkehrsteilnehmern mit Hilfe von LiDAR im Kontext des autonomen Fahrens
extern
BA / FP Implementierung einer Radarsignalverarbeitungskette in GNU Radio
extern
FP Praktikanten (m/w) zur Weiterentwicklung eines Human-Machine-Interfaces mit Gestensteuerung
extern

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Preise & Auszeichnungen

  • J. Fuchs, ARGUS Award 2018, HENSOLDT Sensors GmbH, 2018. [Bibtex]
    @prize{fuchs_prize_2018,
    abstract = {Masterarbeit: Implementierung und Evaluierung von Verfahren zur Winkelschätzung mittels Neuronaler Netze für FMCW-Radarsysteme},
    author = {Fuchs, Jonas},
    booktitle = {HENSOLDT Sensors GmbH},
    cris = {fuchs_prize_2018},
    year = {2018},
    title = {ARGUS Award 2018},
    type = {20773-Kleiner Preis},
    }
  • J. Fuchs, SEMIKRON Student Award, SEMIKRON International GmbH, 2013. [Bibtex]
    @prize{fuchs_prize_2013,
    author = {Fuchs, Jonas},
    booktitle = {SEMIKRON International GmbH},
    cris = {fuchs_prize_2013},
    year = {2013},
    month = {07},
    day = {12},
    title = {SEMIKRON Student Award},
    type = {20773-Kleiner Preis},
    }

COPYRIGHT NOTICE: Copyright and all rights of the material above are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by the appropriate copyright. The material may not be reposted without the explicit permission of the copyright holder.

COPYRIGHT NOTICE FOR IEEE PUBLICATIONS: © IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

COPYRIGHT NOTICE FOR EUMA PUBLICATIONS: © EUMA. Personal use of this material is permitted. Permission from European Microwave Association(EUMA) must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Publikationen

2019

  • J. Fuchs, R. Weigel, and M. Gardill, "Single-Snapshot Direction-of-Arrival Estimation of Multiple Targets using a Multi-Layer Perceptron" in 2019 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), Detroit, MI, USA, 2019, pp. 1-4. [DOI] [Bibtex]
    @inproceedings{fuchs2019a,
    abstract = {An alternative approach to high-resolution direction-of-arrival estimation in the context of automotive FMCW signal processing is shown by training a neural network with simulation as well as experimental data to estimate the mean and distance of the azimuth angles from two targets. Testing results are post-processed to obtain the estimated azimuth angles which can be validated afterwards. The performance of the proposed neural network is then compared with a reference implementation of a maximum likelihood estimator. Final evaluations show super-resolution like performance with significantly reduced computation time, which is expected to have an impact on future multi-dimensional high-resolution DoA estimation.},
    author = {Fuchs, Jonas and Weigel, Robert and Gardill, Markus},
    language = {English},
    booktitle = {2019 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM)},
    cris = {https://cris.fau.de/converis/publicweb/publication/211247235},
    year = {2019},
    month = {06},
    day = {03},
    doi = {10.1109/ICMIM.2019.8726554},
    eventdate = {2019-04-15/2019-04-16},
    faupublication = {yes},
    keywords = {Direction-of-arrival estimation;Training;Neural networks;Radar;Maximum likelihood estimation;Signal to noise ratio;Automotive Radar;Direction-of-Arrival Estimation;Neural Network;Multi-Layer Perceptron},
    pages = {1--4},
    peerreviewed = {unknown},
    title = {Single-Snapshot Direction-of-Arrival Estimation of Multiple Targets using a Multi-Layer Perceptron},
    type = {Konferenzschrift},
    venue = {Detroit, MI, USA},
    }
  • M. Gardill, J. Schwendner, and J. Fuchs, "In-Situ Time-Frequency Analysis of the 77 GHz Bands using a Commercial Chirp-Sequence Automotive FMCW Radar Sensor" in 2019 IEEE MTT-S International Microwave Symposium (IMS), Boston, MA, USA, 2019, pp. 544-547. [Bibtex]
    @inproceedings{gardill2019,
    abstract = {A commercial chirp-sequence automotive radar sensor is reconfigured to allow for time-frequency analysis of the 77GHz automotive radar bands. Compared to highend measurement equipment such as real-time spectrum analyzers, due to its low cost and compact size the proposed approach allows for in-situ interception and time-frequency analysis of traffic scenarios, e.g. by mounting the sensor in a test vehicle exactly at the positions where the radar sensors would be mounted. The theory behind the modification is discussed and compared with measurements. Time-frequency spectra of a rural road driving scenario obtained with the proposed system are shown to illustrate the practical relevance and usefulness of the approach.},
    author = {Gardill, Markus and Schwendner, Johannes and Fuchs, Jonas},
    language = {English},
    booktitle = {2019 IEEE MTT-S International Microwave Symposium (IMS)},
    cris = {https://cris.fau.de/converis/publicweb/publication/210771629},
    year = {2019},
    month = {05},
    day = {07},
    eventdate = {2019-06-02/2019-02-07},
    faupublication = {yes},
    keywords = {radar; automotive; FMCW; chirp-sequence; time-frequency analysis},
    pages = {544--547},
    peerreviewed = {unknown},
    title = {In-Situ Time-Frequency Analysis of the 77 GHz Bands using a Commercial Chirp-Sequence Automotive FMCW Radar Sensor},
    type = {Konferenzschrift},
    url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8700983&isnumber=8700641},
    venue = {Boston, MA, USA},
    }
  • J. Fuchs, R. Weigel, and M. Gardill, "Model Order Estimation using a Multi-Layer Perceptron for Direction-of-Arrival Estimation in Automotive Radar Sensors" in 2019 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet), Orlando, Florida, USA, 2019, pp. 1-3. [DOI] [Bibtex]
    @inproceedings{fuchs2019,
    abstract = {In this work, a machine-learning-based approach to decide whether one or two targets are present in the same range-velocity cell of a chirp-sequence FMCW radar system is evaluated. An experimental setup for generating sufficient large sets of training and testing data using real measurement data from automotive 77 GHz radar sensors is presented. Using this data a multi-layer perceptron is trained to directly estimate the number of present targets from the received signals in order to determine if resolution in the spatial domain is necessary. Evaluations of the trained model show that the network is able to inherently learn the underlying signal model and reach super-resolution performance.
    }, author = {Fuchs, Jonas and Weigel, Robert and Gardill, Markus}, language = {English}, booktitle = {2019 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet)}, cris = {https://cris.fau.de/converis/publicweb/publication/203918515}, year = {2019}, month = {01}, day = {20}, doi = {10.1109/WISNET.2019.8711806}, eventdate = {2019-01-20/2019-01-23}, faupublication = {yes}, keywords = {Automotive Radar; Direction-of-Arrival Estimation; Neural Network; Multi-Layer Perceptron}, pages = {1--3}, peerreviewed = {unknown}, title = {Model Order Estimation using a Multi-Layer Perceptron for Direction-of-Arrival Estimation in Automotive Radar Sensors}, type = {Konferenzschrift}, venue = {Orlando, Florida, USA}, }

2018

  • M. Gardill, J. Fuchs, C. Frank, and R. Weigel, "A Multi-Layer Perceptron Applied to Number of Target Indication for Direction-of-Arrival Estimation in Automotive Radar Sensors" in 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), Aalborg, Denmark, 2018, pp. 1-6. [DOI] [Bibtex]
    @inproceedings{gardill2018,
    author = {Gardill, Markus and Fuchs, Jonas and Frank, Christian and Weigel, Robert},
    language = {English},
    booktitle = {2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)},
    cris = {https://cris.fau.de/converis/publicweb/publication/200736205},
    year = {2018},
    month = {10},
    day = {17},
    doi = {10.1109/MLSP.2018.8516952},
    eventdate = {2018-09-17/2018-09-20},
    faupublication = {yes},
    keywords = {Automotive Radar; Direction-of-Arrival Estimation; Neural Network; Multi-Layer Perceptron},
    pages = {1--6},
    peerreviewed = {Yes},
    title = {A Multi-Layer Perceptron Applied to Number of Target Indication for Direction-of-Arrival Estimation in Automotive Radar Sensors},
    type = {Konferenzschrift},
    venue = {Aalborg, Denmark},
    }
  • C. Will, K. Shi, S. Schellenberger, T. Steigleder, F. Michler, J. Fuchs, R. Weigel, C. Ostgathe, and A. Koelpin, "Radar-Based Heart Sound Detection", Scientific Reports, 2018. [DOI] [Bibtex]
    @article{will2018,
    abstract = {This paper introduces heart sound detection by radar systems, which enables touch-free and continuous monitoring of heart sounds. The proposed measurement principle entails two enhancements in modern vital sign monitoring. First, common touch-based auscultation with a phonocardiograph can be simplified by using biomedical radar systems. Second, detecting heart sounds offers a further feasibility in radar-based heartbeat monitoring. To analyse the performance of the proposed measurement principle, 9930 seconds of eleven persons-under-tests' vital signs were acquired and stored in a database using multiple, synchronised sensors: a continuous wave radar system, a phonocardiograph (PCG), an electrocardiograph (ECG), and a temperature-based respiration sensor. A hidden semi-Markov model is utilised to detect the heart sounds in the phonocardiograph and radar data and additionally, an advanced template matching (ATM) algorithm is used for state-of-the-art radar-based heartbeat detection. The feasibility of the proposed measurement principle is shown by a morphology analysis between the data acquired by radar and PCG for the dominant heart sounds S1 and S2: The correlation is 82.97 ± 11.15% for 5274 used occurrences of S1 and 80.72 ± 12.16% for 5277 used occurrences of S2. The performance of the proposed detection method is evaluated by comparing the F-scores for radar and PCG-based heart sound detection with ECG as reference: Achieving an F1 value of 92.22 ± 2.07%, the radar system approximates the score of 94.15 ± 1.61% for the PCG. The accuracy regarding the detection timing of heartbeat occurrences is analysed by means of the root-mean-square error: In comparison to the ATM algorithm (144.9 ms) and the PCG-based variant (59.4 ms), the proposed method has the lowest error value (44.2 ms). Based on these results, utilising the detected heart sounds considerably improves radar-based heartbeat monitoring, while the achieved performance is also competitive to phonocardiography.
    }, author = {Will, Christoph and Shi, Kilin and Schellenberger, Sven and Steigleder, Tobias and Michler, Fabian and Fuchs, Jonas and Weigel, Robert and Ostgathe, Christoph and Koelpin, Alexander}, cris = {https://cris.fau.de/converis/publicweb/publication/202373734}, year = {2018}, month = {07}, day = {26}, doi = {10.1038/S41598-018-29984-5}, faupublication = {yes}, issn = {2045-2322}, journaltitle = {Scientific Reports}, peerreviewed = {Yes}, title = {Radar-Based Heart Sound Detection}, type = {online publication}, url = {https://www.nature.com/articles/s41598-018-29984-5}, }

2017

  • C. Will, M. Sporer, N. Sebald, J. Fuchs, R. Weigel, and A. Koelpin, "Radarbasiertes Structural Health Monitoring der Rotorblätter von Windkaftanlagen" in Kleinheubacher Tagung, Miltenberg, Germany, 2017. [Bibtex]
    @inproceedings{will2017b,
    author = {Will, Christoph and Sporer, Michael and Sebald, Nina and Fuchs, Jonas and Weigel, Robert and Koelpin, Alexander},
    publisher = {URSI},
    booktitle = {Kleinheubacher Tagung},
    cris = {https://cris.fau.de/converis/publicweb/publication/123490664},
    year = {2017},
    month = {09},
    day = {25},
    eventdate = {2017-09-25/2017-09-27},
    faupublication = {yes},
    peerreviewed = {Yes},
    title = {Radarbasiertes Structural Health Monitoring der Rotorblätter von Windkaftanlagen},
    type = {Abstract zum Vortrag},
    venue = {Miltenberg, Germany},
    }

COPYRIGHT NOTICE: Copyright and all rights of the material above are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by the appropriate copyright. The material may not be reposted without the explicit permission of the copyright holder.

COPYRIGHT NOTICE FOR IEEE PUBLICATIONS: © IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

COPYRIGHT NOTICE FOR EUMA PUBLICATIONS: © EUMA. Personal use of this material is permitted. Permission from European Microwave Association(EUMA) must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.