Master Thesis: Generating realistic radar video data using AI and GANMaster thesis work i Göteborg

Master thesis work at SAAB Surveillance Radar Engineering/X Innovation Lab 2020

Generative Adversarial Networks (GAN) is a class of machine learning systems where two neural networks compete against each other. The goal is to train a network to generate data to fool the other network into believing the generated data is real rather than artificial.

Developing modern radar systems is a long and complex process that is made even longer by the fact that simulating the signal environment in which the radar will operator is very difficult. Traditional methods of generating test data is too predictable and a system typically needs many hours of real world testing to find bugs and evaluate performance.

1.       Can GAN be used to generate radar data from a textual scenario specification?

2.       Can GAN be used to generate data in real time?

3.       Is the data generated of such quality that it can be used to test real radar application software?

4.       Is the data generated of such quality that it can be used to train other AI applications?

The goal of this master thesis work is to build a GAN that can generate raw radar data from a specification file. The generated data will then be used to test the software of the radar system and potentially also to train other AI to classify objects.

The work will be divided into two parts. The first is a study of GAN to gain a deeper understanding of the technology and what is currently used in industry and the academic world. Based on this research, the students will then propose an implementation of GAN and proceed to implement this to demo the performance.

The work is expected to deliver the following:

a)       An overview of current research with regards to GAN.

b)      An overview of available tools and libraries that can be used to work with GAN at SAAB

c)       A demo of a GAN that can produce raw radar data.

d)      Suggestions on how to proceed, either with further studies or industrialization of the work done

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