Master Thesis: Drone Detection with Acoustic and ISAC Sensing
Ericsson ABNy
Sammanfattning
This opportunity involves a thesis project focused on enhancing drone detection through the integration of acoustic sensing and Integrated Sensing and Communication (ISAC) technologies. The role allows for exploration of various aspects of drone detection, including performance comparisons, acoustic signature analysis, and AI applications. Candidates will work in a research environment, contributing to the development of multi-modal sensing systems.Ansök senast: Öppet tillsvidare
Publicerad: 2026-11-03
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About this opportunity
Drones are everywhere now - delivering packages, inspecting infrastructure, supporting public safety - and that creates a real need to reliably detect, classify, and track them. At the same time, 6G is set to turn radio networks into sensors through Integrated Sensing and Communication (ISAC), letting the network itself detect and localize objects around it.
But drones are tricky targets: they fly low, they're small, and they hide in cluttered environments. Recent real-world deployments have shown that dense networks of cheap microphones can detect drones surprisingly well by listening for their motors and propellers.
This thesis explores how these two very different sensing worlds - acoustic and radio-based ISAC - can work together.
What you will do
You'll investigate acoustic sensing and ISAC as complementary technologies for drone detection, figuring out where each one shines and where they fall short. You will have freedom to define areas of exploration within the project.
These could include, but are not limited to:
- Comparing detection performance, extractable information, and ideal operating conditions for acoustic vs. ISAC sensing.
- Recording and analyzing drone acoustic signatures, and experimenting with microphone arrays and direction-finding.
- Classifying drones from their sound, and using acoustic cues to identify a drone after an ISAC detection event.
- Exploring AI for detection, classification, and sensor fusion - including how to combine confidence from different modalities and dynamically trust the most reliable one.
- Evaluating cost-versus-performance trade-offs, especially how to balance a few advanced sensing nodes against many inexpensive acoustic sensors.
The goal is a set of recommendations for future AI-enabled, multi-modal drone sensing systems.
The skills you bring
- Knowledge in Computer Science, Electrical Engineering, Signal Processing, or a related field.
- Experience in at least one programming language (Python is preferred).
- Eagerness to learn and explore new concepts.
- Bonus: experience in machine learning, signal processing, or wireless/acoustic systems.
About this opportunity
Drones are everywhere now - delivering packages, inspecting infrastructure, supporting public safety - and that creates a real need to reliably detect, classify, and track them. At the same time, 6G is set to turn radio networks into sensors through Integrated Sensing and Communication (ISAC), letting the network itself detect and localize objects around it.
But drones are tricky targets: they fly low, they're small, and they hide in cluttered environments. Recent real-world deployments have shown that dense networks of cheap microphones can detect drones surprisingly well by listening for their motors and propellers.
This thesis explores how these two very different sensing worlds - acoustic and radio-based ISAC - can work together.
What you will do
You'll investigate acoustic sensing and ISAC as complementary technologies for drone detection, figuring out where each one shines and where they fall short. You will have freedom to define areas of exploration within the project.
These could include, but are not limited to:
- Comparing detection performance, extractable information, and ideal operating conditions for acoustic vs. ISAC sensing.
- Recording and analyzing drone acoustic signatures, and experimenting with microphone arrays and direction-finding.
- Classifying drones from their sound, and using acoustic cues to identify a drone after an ISAC detection event.
- Exploring AI for detection, classification, and sensor fusion - including how to combine confidence from different modalities and dynamically trust the most reliable one.
- Evaluating cost-versus-performance trade-offs, especially how to balance a few advanced sensing nodes against many inexpensive acoustic sensors.
The goal is a set of recommendations for future AI-enabled, multi-modal drone sensing systems.
The skills you bring
- Knowledge in Computer Science, Electrical Engineering, Signal Processing, or a related field.
- Experience in at least one programming language (Python is preferred).
- Eagerness to learn and explore new concepts.
- Bonus: experience in machine learning, signal processing, or wireless/acoustic systems.
Ansök till tjänsten
Master Thesis: Drone Detection with Acoustic and ISAC Sensing
Ny
OM FÖRETAGET
Ericsson AB
