Multi-task Learning for Radar Signal Characterisation
Zi Huang Akila Pemasiri Simon Denman Clinton Fookes Terrence Martin
Radio signal recognition is a crucial task in both civilian and military
applications, as accurate and timely identification of unknown signals is an
essential part of spectrum management and electronic warfare. The majority of
research in this field has focused on applying deep learning for modulation
classification, leaving the task of signal characterisation as an understudied
area. This paper addresses this gap by presenting an approach for tackling
radar signal classification and characterisation as a multi-task learning (MTL)
problem. We propose the IQ Signal Transformer (IQST) among several reference
architectures that allow for simultaneous optimisation of multiple regression
and classification tasks. We demonstrate the performance of our proposed MTL
model on a synthetic radar dataset, while also providing a first-of-its-kind
benchmark for radar signal characterisation.