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
In the aviation sector, a vast network of emitters, both on the ground and in the air, is utilised to emit radio waves for communication and radar purposes. This thesis proposal focuses on the identification aspect of Electronic Warfare (EW), specifically Electronic Support (ES). A key component of ES systems is the Radar Warning Receiver (RWR), which passively detects and classifies radar signals to warn pilots of potential threats. The classification of these signals is crucial for deploying appropriate countermeasures.
Electronic Warfare (EW): EW involves exploiting the electromagnetic spectrum to disturb, identify, or neutralize emitting sources. This can include jamming communications, disrupting radar systems, or identifying and locating enemy emitters. EW is a critical component of modern military operations, providing both offensive and defensive capabilities.
Electronic Support (ES): ES systems, particularly Radar Warning Receivers (RWRs), play a vital role in EW by intercepting and classifying radar signals. RWRs passively listen for emitted radar signals and warn pilots of their presence. The source of these signals can range from air traffic control centres to incoming missiles, making accurate classification essential for deploying justifiable countermeasures.
Signal Characteristics: The classification of radar signals typically relies on several key characteristics:
Radio Frequency (RF): The frequency at which the radar signal is transmitted.
Pulse Width (PW): The duration of the radar pulse.
Pulse Repetition Interval (PRI): The time between successive radar pulses.
Machine Learning in EW: Machine learning has shown promise in automating the classification of radar emitters. Supervised learning techniques have achieved up to 90% accuracy in classifying declassified emitters. Recently, Bayesian Gaussian Mixture Models (GMMs) have been used for classifying tracks and emitters. GMMs offer the advantage of learning weights (e.g., medians and standard deviations) that can define new emitter characteristics, making them a powerful tool for EW.
Current Challenges:
Manual Construction of EW Libraries: EW libraries, which contain known and newly discovered emitters, are traditionally constructed manually. This process is labour-intensive and involves experts manually discovering and transferring emitter data to libraries. The manual overhead can be significant, and the process is prone to human error.
Complex Emitter Characteristics: More complex emitters often use certain patterns of RF, PW, and PRI in the temporal domain. Modelling these complex characteristics requires advanced techniques that can capture the temporal dynamics of the signals.
Description of the master thesis
This thesis proposes the use of GMMs to automate the construction of EW libraries. GMMs can learn weights that define new emitter characteristics, reducing the manual overhead and improving the accuracy and efficiency of the library construction process.
Complex Characteristic Modelling with Hidden Markov Models (HMMs): Additionally, HMMs can be employed to model more complex emitter characteristics in the temporal domain. HMMs are well-suited for capturing the sequential dependencies in the signal characteristics, providing a more comprehensive model of the emitters.
Automation of EW Library Construction:
Utilise the weights defined by GMMs to automate the construction of EW libraries.
Reduce manual overhead and improve the accuracy and efficiency of the library construction process.
Complex Characteristic Modelling:
Explore the use of HMMs to model more complex emitter characteristics in the temporal domain.
Capture the sequential dependencies in the signal characteristics to provide a more comprehensive model of the emitters.
Our software development teams at Tactical Functions develop desktop applications, supporting our Electronic Warfare systems. Our products include analysis and visualization of data and real time feeds, big data, secure user transactions, amongst many other things. Read more about electronic warfare and products from SAAB here.
Your profile
This master of science thesis is suitable for one or two students, with an interest in software development, AI, machine learning and/or signal processing.
You are at the end of your Master of Science in e.g., Computer Science, AI Engineering, Information Technology or equivalent and about to start your Master Thesis work for 30 HP.
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 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 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 defence and security company with an enduring mission, to help nations keep their people and society safe. Empowered by its 26,100 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
16-11-2025
Contact information
Jenny Lagerlöf, Manager