• Themenbeschreibung: One of the key innovation factors of fifth generation communication (“5G”) is the extension to millimeter wave (mm-waves) frequency region in order to meet the demand for exponentially increasing data rates. However, steerable high gain antennas are required to overcome the increased path loss at mm-waves and to ensure exact beam alignment between communication partners.
    In this work a steerable antenna array for base station application has to be designed in order to enable possible illumination of one 90°-sector with an overlapping beam scheme. For the array elements excitation with variable phases and amplitudes dedicated chips are provided that need to be wired/ connected properly.
    The scope of this work includes
    - Literature research in phased array design and mm-wave 5G communication
    - Antenna element design for 26.5 -29.5 GHz
    - Antenna array design and layout (including beam-steering scheme)
    - Implementation and measurement of antenna array in anechoic chamber

    Applicants should bring the following
    - Thorough understanding of Electrodynamics, RF and Antennas
    - Experience with industry-standard EMC-Tools
    - Interest in manual working and crafting
    - Good data processing skills
    - Willingness to work self-dependent
    The thesis can be written in English or German.

  • Themengebiete: Antenna, Antenna Array, Antenna measurement, 5G, Beam-steering
  • Voraussetzungen:
  • Betreuer: Christian Steinmetz
  • Hochschullehrer:
  • PDF: Aushang

  • Themenbeschreibung: Spoofing is the transmission of fake Global Navigation Satellite System (GNSS) signals. It is a
    malicious attack, which misleads a GNSS receiver to calculate the wrong position and time. A receiver,
    which successfully detects a spoofing event, can warn a user that the position and time cannot be
    trusted. Therefore, reliable spoofing detection is necessary for GNSS integrity.
    Previous work on spoofing detection implemented rudimentary machine learning methods. It showed
    that training on simulated data facilitates good performance when evaluating real recorded data.
    However, there were several limitations in this study. First, the lack of variance in the real recorded data
    was insufficient to allow successful training on the data and was adequate to evaluate the data. Second,
    feature engineering was omitted, and leaves room for improvement. Furthermore, no sufficient studies
    on the use of deep neural networks have been conducted.
    This topic aims to improve spoofing detection by advanced machine learning techniques, including
    feature optimization and deep learning methods. The evaluation is focused on generalization over
    datasets, reliability and computational complexity.
  • Literature review on GNSS, GNSS spoofing, antenna arrays, feature engineering, and supervised machine learning
  • Analysis of provided datasets and identification of features
  • Identification of suitable machine learning methods (feature based or deep learning)
  • Implementation and optimization of the identified machine learning methods
  • Evaluation of results
  • Documentation of results and final thesis
  • Themengebiete: Global Navigation Satellite System, GNSS, Globas Positioning System, GPS,Machine Learning
  • Voraussetzungen: Basic knowledge of satellitenavigation and antenna theory/Knowledge of classification methodology and machine learning approaches/Familiarity with Python and machine learning frameworks such as tensorflow or scikit?learn
  • Betreuer: Johannes Rossouw van der Merwe
  • Hochschullehrer: Jörn Thielecke
  • PDF: Aushang
    • Themenbeschreibung: Low earth orbit (LEO) positioning, navigation and timing (PNT) has become a hot topic for
      satellite?based navigation. LEO mega?constellations, i.e., constellations exceeding 1000 satellites, provide
      excellent coverage and a good dilution of precision (DOP) for positioning. The much lower altitude – in
      comparison to medium earth orbit (MEO) or geo?synchronous orbit (GSO) global navigation satellite
      systems (GNSS) – also provides improved path?loss, yielding better signal to noise ratios (SNRs) for
      detection, acquisition, and tracking. Furthermore, LEO satellites have high velocity; hence, allowing
      Doppler?based positioning. Lastly, the LEO constellations selected for PNT are primarily communication
      satellites, which makes them much less prone to spoofing attacks, in comparison to the predictable GNSS
      satellites. In conclusion, LEO PNT is a good alternative or complimentary approach to legacy satellite
      LEO PNT does have several issues, as these signals are not designed for PNT. First, alternative acquisition
      and tracking methods are required to extract appropriate observables from the satellites for navigation.
      Second, these satellites do not necessarily provide their ephemeris information (orbital data) nor are
      these precise, when they do. Therefore, accurate ephemeris information needs to be estimated. Third,
      many LEO satellites do not necessarily have a timing reference; hence, this should also be determined.
      Lastly, LEO satellites rarely have high precision atomic clocks like GNSSs, requiring additional clock
      corrections. This project focuses on the first challenge, of designing and developing alternative
      acquisition and tracking methods.
      As an initial case study, Iridium is proposed. The Iridium downlink is in the 1.6 GHz frequency band,
      which is adjacent to the L1 GNSS band. Therefore, state?of?the?art GNSS antennas, and modified GNSS
      recording receivers may be used for a comparable evaluation. Although, Iridium is proposed, the student
      may identify other suitable LEO constellations and adapt the project accordingly.
    • Literature review on GNSS, LEO-PNT methods, and acquisition-tracking methods
    • Analysis of opportunistic LEO constellations and selection of a suitable constellation
    • Signal recording and analysis of the selected LEO constellation
    • Identification of suitable acquisition-tracking architecture and extraction of observables
    • Implementation and of an acquisition-tracking architecture to show that a satellite can be used
  • Themengebiete: Global Navigation Satellite System, GNSS, Low Earth Orbit (LEO) Positioning, Navigation and Timing (PNT)
  • Voraussetzungen: Basic knowledge of satellite?navigation and tracking filters/Good knowledge on signal processing/Familiarity with Python and/or Matlab
  • Betreuer: Johannes Rossouw van der Merwe
  • Hochschullehrer: Jörn Thielecke
  • PDF: Aushang
    • Themenbeschreibung: Global Navigation Satellite Systems (GNSSs) provide global positioning capabilities to any
      capable receiver — whether integrated into smartphones or used for aeronautical navigation. An issue with
      GNSS signals are their low received signal power on earth’s surface, which makes them sensitive to
      interference signals. Other licensed spectrum users, the result of old or poorly designed equipment leaking
      in from adjacent frequency bands, opportunistic use of the spectrum by radio operators, or even
      purposefully designed interferences, may be the cause interference signals. Therefore, it is crucial for the
      local service quality to monitor the GNSS frequency bands and to report any interference signal to the
      appropriate authorities.
      The DARCY system consists of a network of low-cost sensor nodes to monitor the GNSS spectrum over a
      larger geographic area. Each node consists of e.g. a Raspberry Pi Single Board Computer (SBC), a low-cost
      GNSS receiver to monitor the GNSS signal quality, and a Software Defined Receiver (SDR) Frontend to
      monitor the spectrum. The sensor nodes monitor and report any data to a server, which does interference
      detection, interference localization, and reporting of any suspicious activity.
      This master’s project focuses on algorithm development for collaborative interference localization. It uses
      the DARCY sensor nodes to locate potential interference.
    • Literature review on GNSS, interference localization, Received Signal Strength (RSS) positioning, Time Difference of Arrival (TDoA) positioning, collaborative techniques, and information fusion
    • Analysis of interference localization methods and data available from the sensor nodes
    • Design and implementation of localization algorithm(s)
    • Setup of simulation environment to test selected algorithms
    • Field recording with interferences and sensor nodes
  • Themengebiete: Global Navigation Satellite System, Interference Localization, Received Signal Strength (RSS) Positioning
  • Voraussetzungen: Basic knowledge of satellite-navigation/Good knowledge of signal processing and estimation theory/Experience with Kalman Filtering is a bonus/Python and/or Matlab
  • Betreuer: Johannes Rossouw van der Merwe
  • Hochschullehrer: Jörn Thielecke
  • PDF: Aushang



    Anfragen zu Themen und der Betreuung einer MA-Arbeit können sie auch unabhängig von den ausgeschriebenen Themen direkt an die Ansprechpartner der jeweiligen Forschungsschwerpunkte schicken.


    Dr.-Ing. Jörg Robert

    Am Wolfsmantel 33
    91058 Erlangen - Tennenlohe


    Dipl.-Ing. Jürgen Frickel

    Am Wolfsmantel 33 / 3.OG
    91058 Erlangen - Tennenlohe

    Navigation und Ortsbestimmung / Autonome Robotik

    Adam Kalisz

    Am Wolfsmantel 33 / 3. OG
    91058 Erlangen - Tennenlohe

    Navigation und Ortsbestimmung / Funkortung

    Sebastian Kram

    Am Wolfsmantel 33 / 3. OG
    91058 Erlangen - Tennenlohe

    Navigation und Ortsbestimmung / GPS und Allg.

    Prof. Dr.-Ing. Jörn Thielecke

    Am Wolfsmantel 33 - 3.OG
    91058 Erlangen - Tennenlohe