Master Thesis – Machine Learning for Radar Interference mitigation

Category: Machine Learning, FMCW Radar, Programming

Scope: 2 students completing 30 credits (20 weeks) each 

Background
Axis is developing radar products for public surveillance. These radars operate on a shared frequency band and the risk of being disturbed/interfered by other radars (or units transmitting in the same band) is a well-known problem. However, it is not always easy to find the correct algorithm to cope with this since the type of the interferers can’t be known in advance. We would like to examine if general an AI-based algorithm can be trained to solve this problem.

 Goal

• learn how a modern radar operates,
• learn about radar signal interference,
• do research on AI algorithms
• build a simulator for the problem,
• test your conclusions on a real Axis radar system.

We expect you to have an interest in math, decent knowledge of programming in Python, and a basic understanding of neural networks. You can apply for this thesis work on your own or as a team of two.

OK, I am interested! What do I do now?
You are valuable to us – how nice that you are interested in one of our proposals! There are a few things for you to keep in mind when applying.

• Applications are accepted in both Swedish and English, and you apply via the proposal advert.
• The announced theses are open only to students affiliated with a Swedish university/college either directly or via an exchange program.
• When the thesis proposal states that it includes two students working together, we would like you to apply in pairs. In these cases, send one application each but make sure to clearly state in your application who your co-applicant is. If you have any questions regarding this, please do not hesitate to contact us.
• Please attach your CV and University/college grade summary. 

Contact information
For more information or questions please contact Anders Mannesson at +46 46 272 1403 or anders.mannesson@axis.com