Thesis work - Stochastic Modelling of Advanced Multi-Functional Radars using Reinforcement Learning for Electronic Warfare Applications

Are you a student eager to apply your theoretical knowledge and fresh perspectives to real-world challenges? At Saab, we believe that innovation thrives on new ideas, and your master thesis project could be the spark that ignites our next technological breakthrough.

Your role

We recognize the immense value that students bring to our company. Your academic rigor, combined with your enthusiasm for cutting-edge technology, allows you to approach problems with a unique and insightful lens. At Saab, you'll have the opportunity to collaborate with experienced engineers and specialists, gaining invaluable practical experience while making a tangible contribution to our growth and development.

Background

Electronic Warfare (EW) involves the use of electromagnetic spectrum to sense, disrupt, deceive, or deny an enemy's use of the spectrum while protecting one's own use. This is traditionally done using rule-based approaches but as the complexity of radars and communication equipment continually advances, obtaining a complete situational picture becomes increasingly challenging. To develop enhanced, data-driven algorithms, it is important to also model advanced emitters, with a particular focus on multi-functional hostile radars.

Description of the Thesis Project

Advanced Multi-Functional Radars (MFRs) are characterized by their ability to rapidly adapt their behavior and function based on the operational environment. A radar's main purpose is to detect, locate, and track targets. This is accomplished by selecting appropriate waveforms and scan patterns.

Historically, the emitted waveforms from radars have been limited by physics and hardware constraints. However, as hardware capabilities have advanced, so has the ability of radars to generate increasingly complex waveforms. The radars do, however, remain constrained by certain physical and design limitations, making it possible to analyze the signal.

The radar scan patterns are also becoming more sophisticated with the introduction of Active Electronically Scanned Arrays (AESA), which can steer the antenna beam in any direction instantaneously. Unlike traditional scanning antennas, AESA radars do not require physical movement of the antenna. This allows AESA radars to adapt the time spent on target more efficiently based on the measurements and state of the target.

This work will explore various approaches to model advanced MFRs, where the time on target and waveform can be adapted based on either rule-based algorithms or a trained agent. The objective is not to model the emitter at the Radio Frequency (RF) level but rather to stochastically capture sequences of latent description of the pulses that an MFR would emit.

This thesis will be conducted with input from three different teams/projects. The first team develops solutions that enable AI development in the EW domain, the second team focuses on AI research and development within the same domain, and the third team is from Saab’s radar division in Gothenburg.

Your profile

This project is suitable for 1-2 students in the final stage of their education, with an interest in Physics, Applied Mathematics, and Machine Learning, who are about to begin a 30-hp thesis project.

We provide the support and guidance you need to translate your theoretical knowledge into practical solutions. Join us and become a driving force behind Saab's technological advancements!

This position requires you to work on-site at Saab’s Solna Strand office in Stockholm. This position also 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.

Kindly observe that this is an ongoing recruitment process and that the position might be filled before the closing date of the advertisement.

Contact Information

If you have any more questions or need further clarification, do not hesitate to get in touch.

Axel Nilsson, Master Thesis Supervisor

Tel: +46 73-44 66 928

E-mail: axel.nilsson2@saabgroup.com

Alexander Lindmaa, Master Thesis Supervisor

Tel: +46 73-41 83 736

E-mail: alexander.lindmaa@saabgroup.com

Ulrica Linander, Manager

E-mail: ulrica.linander@saabgroup.com

What you will be a part of

Explore a wealth of possibilities. Take on challenges, create smart inventions, and grow beyond. This is a place for curious minds, brave pioneers, and everyone in between. Together, we achieve the extraordinary, each bringing our unique perspectives. Your part matters.

Saab is a leading defense and security company with an enduring purpose, to help nations keep their people and society safe. Empowered by its 28,000 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 defense capability of several nations. Read more about us here.