Master's thesis: AI Driven 3D Radar for Drone Detection with 6G ISAC

Small, low-flying drones are difficult for conventional radar to detect, creating challenges around airports, infrastructure and public events. Within RADAR PAN-SWEDEN, this thesis will move an existing radar emulation framework into physical experiments using 77 GHz FMCW radar modules. The work explores distributed drone detection by combining radar processing, edge AI and 5G connectivity in the context of future 6G Integrated Sensing and Communication (ISAC).

The assignment

• Review 6G ISAC, distributed multistatic radar and RF-based drone detection, then specify and integrate radar hardware with researchers at TU Graz.
• Plan and conduct measurements of commercial multi-rotor drones and background clutter, collecting raw radar data and micro-Doppler signatures.
• Implement and benchmark range, Doppler and azimuth FFT processing and adaptive CFAR detection on embedded edge hardware such as NVIDIA Jetson.
• Deploy, evaluate and fine-tune an existing CNN detection model using the collected data for real-time operation.
• Connect radar nodes through 5G modems for low-latency transmission of detections to a central server, and help expand the system to at least five measurement nodes at Swedish test sites.

Who we are looking for

• Master’s student in Electrical Engineering, Telecommunications, Computer Science, Mechatronics or a related discipline.
• Strong grounding in digital signal processing, linear algebra and radar fundamentals, including FMCW, FFT processing and CFAR.
• Good programming ability in Python and/or C/C++.
• Experience with MATLAB/Simulink, embedded Linux or edge platforms such as NVIDIA Jetson is advantageous.
• Interest in sensor fusion, state estimation such as Kalman filtering, and applied machine learning is a plus.
• Enjoys practical hardware experiments, communicates well in English and can work independently within an international team.

What we offer

• Close research collaboration with RISE and the Institute of Microwave and Photonic Engineering at TU Graz.
• Hands-on work with physical radar modules, embedded AI platforms and a distributed drone-detection testbed.

Practical details

Flexible start; 30 credits over approximately 20–30 weeks of full-time work. Based at RISE in Stockholm and TU Graz in Austria. Apply by 22 November 2026 with a short personal letter, CV and recent grades. Applications are reviewed and suitable candidates interviewed on a rolling basis. Questions: Tawfik Lazraq, tawfik.lazraq@ri.se.