at Saab
Full TimeThe thesis aims to explore the potential of detecting radar signals directly from raw IQ data for Electronic Support Measures (ESM) systems, bypassing or reducing the need for traditional preprocessing steps. The ultimate goal is to enhance both real-time response and detection accuracy by leveraging AI techniques. This research could lead to more efficient signal detection mechanisms in complex and high-noise environments.
Background
In modern electronic warfare, ESM systems play a crucial role in detecting and classifying radar signals from a variety of sources. Traditional ESM systems often rely on extensive preprocessing of incoming IQ data, which can introduce latency and reduce overall system efficiency. As adversaries develop increasingly sophisticated radar technologies, the need to enhance signal detection capabilities has become more urgent to maintain a strategic advantage. This has driven the demand for innovative approaches that leverage artificial intelligence to streamline signal processing, thereby enhancing both the speed and accuracy of ESM systems. With advancements in AI and machine learning, there is now an opportunity to explore whether AI models can detect and classify signals directly from raw data, which can potentially reduce latency, and improve the accuracy of the system, which is vital in defense applications where response time is critical.
Thesis Description
The aim of this thesis is to develop and evaluate AI-based algorithms capable of detecting radar signals directly from raw IQ data within ESM systems. The project will involve:
This thesis offers the opportunity to contribute to cutting-edge technology in electronic warfare and defense systems.
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 embark on 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
