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Master Thesis 30 hp: Identifying Radars with PRI tables, possible ML approach

Location
Järfälla, Sweden
Closing date
15 December 2021
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Background

Finding and identifying radars is an essential part of electronic warfare (EW). Typically, radars emit signals in pulses according to simple or complex patterns. The time interval between two pluses is often called pulse repetition interval (PRI). A radar may use one or more PRI patterns. If these are known they can be used to differentiate radars and determine what the radar is doing.

Description of the master thesis

The main problem consists of matching an input PRI pattern to a specific radar. A library of known radars and their possible patterns may be known beforehand but is not guaranteed. Input is of varying length and may be noisy. Noise takes the form of dropped or spurious pulses and the input may be received at any point in a PRI pattern. Part of the project consist of finding or designing one or more approaches which will then be implemented and evaluated.

Possible approaches/suggestions:

  • Deep learning model that matches input PRI patterns to a set of known PRI patterns.
  • Clustering of PRI patterns without the use of known patterns.
  • Traditional comparison based methods capable of handling high loads of data in parallel.
  • Some combination of the above.

Some previous master theses at Saab related to this area:

What you will be a part of

Business unit Surveillance offers solutions for surveillance, decision support, and detection and protection against different types of threats. The product portfolio includes airborne, land based and marine radar systems, signal acquisition and self-protection systems, and command control systems for airborne and naval traffic control. The Software Engineering department develops cutting edge systems for signal monitoring and self-protection for the next generation of Gripen fighter and the GlobalEye platform.

Your profile

The master thesis is suitable for 1-2 students with an interest in Computer Science and/or Machine Learning.

You are at the end of your technical master education in Computer engineering, Computer science, Engineering Physics, Electrical Engineering, or equivalent, and are eligible for your 30 HP degree project.

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.

Contact

Mårten Cederholm, Manager Signal Processing SW
073 4374385
marten.cederholm@saabgroup.com

If you aspire to help create and innovate whilst developing yourself in a challenging team setting, Saab may well have the perfect conditions for you to grow. We pride ourselves on a nurturing environment, where everyone is different yet we share the same goal – to help protect people.