Generating Radar Video Data Using Generative Adversarial Nets
Typ
Examensarbete för masterexamen
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2020
Författare
Kauppinen, Martin
Olsson, David
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
In development of radar tools, for example machine learning for target classification
and simulation of processing chains, there is a need for large amounts of complexvalued
data recorded under real conditions by a radar. However, large and properly
labelled data sets of this kind are time consuming and expensive to collect. Currently
such applications instead use simulated data, or smaller sets of real recorded data,
limiting the development of new applications.
In this thesis, we investigate the possibility to utilise Generative Adversarial Nets
(GANs) to extend existing radar data sets in order to get out of this low data
regime. Existing techniques are combined and further developed for generating
complex-valued radar data, with analysis of the quality of the generated data and
its relevance. We conclude that in this context, GANs could be used to extend
existing radar data sets, though more work is needed to make it perfectly realistic,
which is non-trivial.
Beskrivning
Ämne/nyckelord
neural networks , generative adversarial networks , radar , machine learning , complex valued