Master Thesis: Comparison of Bayesian Estimators for random access UWB positioning

Vacancy-ID: 6552 Place of work: Oberpfaffenhofen Starting date: 15.11.2026 Career level: Student research project and final thesis Type of employment: Part time Duration of contract: 6 months Remuneration:Remuneration is in accordance with the Collective Agreement for the Public Sector - Federal Government (TVöD-Bund). Hint:Eligibility for security clearance and the willingness to undergo a security check pursuant to §§ 8 ff. of the Security Clearance Act (SÜG) are conditions for employment at DLR.

The DLR Institute of Communications and Navigation is dedicated to mission-oriented research in selected areas of communications and navigation. Its work ranges from the theoretical foundations to the demonstration of new procedures and systems in a real environment and is embedded in DLR's Space, Aeronautics, Transport, Security and Digitalization programmes.

What to expect

Ultra wideband (UWB) positioning enables accurate distance estimation between wireless nodes by measuring signal propagation times. A widely used approach is Two Way Ranging (TWR), in which the exchange of multiple messages between two nodes allows them to estimate their distance with high precision while compensating for clock offsets and drifts. Conventional TWR between more than two nodes is often realized using time division multiple access (TDMA). However, TDMA comes with challenges in scheduling messages in short sequence. Applications which require high measurement frequency make random access (RA) approaches particularly attractive, as they avoid these scheduling challenges and also offer greater flexibility in dynamic networks. The main goal of this thesis is to compare RA based Bayesian positioning approaches. The first approach follows the classical two Step approach, in which range measurements are computed from TWR message packets and then used as inputs for a positioning filter. The second approach works directly on the recorded timestamps of the exchanged ranging messages and jointly estimates the nodes’ positions and relevant clock parameters.

Your tasks

• Compare distance-based and time-based Bayesian estimation approaches for different time scales and system dynamics
• Analyze UWB random access packet loss, channel load, and their impact on estimation performance
• Work with both simulations and real-world data

Your profile

• Enrolled in Electrical Engineering, Computer Science, Physics, or a related field
• Strong background in estimation theory, signal processing, and applied mathematics
• Experience in software programming, e.g. Python, C++, MATLAB, or similar
• Independent and structured working style
• Good written and spoken English or German

We offer

DLR stands for diversity, appreciation and equality for all people. We promote independent work and the individual development of our employees both personally and professionally. To this end, we offer numerous training and development opportunities. Equal opportunities are of particular importance to us, which is why we want to increase the proportion of women in science and management in particular. Applicants with severe disabilities will be given preference if they are qualified.

We look forward to getting to know you!

If you have any questions about this position (Vacancy-ID 6552) please contact:

Dr. Christian Gentner

Tel.: +49 8153 28 2890