Master Thesis: Drone Detection with Acoustic and ISAC Sensing

Reliable drone detection is becoming increasingly important as drones take on more roles in society, yet their small size and low flight paths make them difficult to distinguish from their surroundings. Integrated Sensing and Communication (ISAC) could enable future 6G networks to sense and locate objects, while microphone networks offer another way to detect drones through sound. This thesis at Ericsson investigates how acoustic sensing and radio-based ISAC could complement each other in future drone sensing systems.

The assignment

• Investigate the strengths and limitations of acoustic sensing and ISAC for detecting drones, with scope to shape the research focus.
• Potentially compare detection capabilities, available information and suitable operating conditions for the two sensing approaches.
• Potentially collect and analyse drone sounds, test microphone arrays and investigate acoustic direction-finding.
• Explore possible methods for sound-based drone classification and acoustic identification following an ISAC detection.
• Consider AI methods for detection, classification and sensor fusion, including combining confidence estimates and adapting the reliance on each sensing modality.
• Assess possible cost and performance trade-offs between a small number of advanced sensing nodes and larger networks of low-cost microphones.
• Develop recommendations for future AI-enabled drone sensing systems that combine multiple sensing modalities.

Who we are looking for

• Knowledge of computer science, electrical engineering, signal processing or a related discipline.
• Programming experience in at least one language, preferably Python.
• Curiosity and a willingness to explore unfamiliar concepts.
• Experience with machine learning, signal processing, wireless systems or acoustic systems is an advantage.

What we offer

• Flexibility to define areas of investigation within the thesis project.

Practical details

Master’s thesis at Ericsson in Lund.