Master Thesis, 30 HP: AI-driven Target Identification in Passive Radar Using Micro-Doppler Signatures

The thesis aims to explore how artificial intelligence can be applied to identify various targets based on their micro-Doppler signatures in passive radar systems, with the potential to improve target classification accuracy.

Background

In modern electronic surveillance, the ability to accurately identify targets is crucial. Micro-Doppler signatures, which capture the unique movement patterns of objects, offer a promising method for distinguishing between different targets, such as drones, vehicles, and personnel. However, extracting meaningful information from these complex signals remains a significant challenge, especially in passive radar systems. The need for efficient and reliable target identification has driven the development of innovative approaches that leverage artificial intelligence to automatically and accurately classify targets. The use of micro-Doppler signatures is emerging as a promising solution, significantly enhancing the efficiency and reliability of passive radar systems.

Thesis Description

The thesis aims to develop and implement AI-driven algorithms for target identification in passive radar systems by analyzing micro-Doppler signatures. This involves several key objectives:

• Data Collection: Gather simulated and real-world micro-Doppler signatures from various target types under different conditions.
• Model Development: Design and train machine learning models tailored to recognize and differentiate between the subtle variations in micro-Doppler signatures.
• Performance Evaluation: Assess the accuracy, efficiency, and robustness of the developed models in real-world scenarios.

The ultimate goal is to create a reliable AI-based target identification system for passive radar that provide a strategic advantage in surveillance and defense applications.

Your Profile

This work is suitable for a motivated student with an interest in machine learning, signal processing, and physics. Ideal candidates should possess a solid foundation in signal processing, proficiency in programming languages such as Python or MATLAB, and experience with machine learning frameworks like TensorFlow or PyTorch.

You are at the end of your Master’s in Computer Science, Electrical Engineering, or a related field and are about to start your 30 HP thesis project. Specific requirements include completed coursework in machine learning, and signal processing, as well as practical experience with machine learning model development.

This position requires that you pass a security vetting based on the current regulations around/of security protection. For positions requiring security clearance additional obligations on citizenship may apply.

What You Will Be Part Of

Behind our innovations stand the people who make them possible. Brave pioneers and curious minds. Everyday heroes and inventive troubleshooters. Those who share deep knowledge and those who explore sky-high. And everyone in between.  ​

Joining us means making an impact together, contributing in our own unique ways. From crafting complex code and building impressive defence and security solutions to simply sharing a coffee with a colleague, every action counts. We encourage you to take on challenges, to create smart inventions and grow in our friendly and tech-savvy workspace. We have a solid mission to keep people and society safe.

Saab is a leading defence and security company with an enduring mission, to help nations keep their people and society safe. Empowered by its 23,000 talented people, Saab constantly pushes the boundaries of technology to create a safer and more sustainable world.

Saab designs, manufactures and maintains advanced systems in aeronautics, weapons, command and control, sensors and underwater systems. Saab is headquartered in Sweden. It has major operations all over the world and is part of the domestic defence capability of several nations. Read more about us here

Last application day

31-11-2024

Contact information

Magnus Thors, Manager

073-437 4797

Kevin Arnmark, Master Thesis Supervisor

073-437 8907