Multi-task Learning for Radar Signal Characterisation

Radio signal recognition is a crucial task in both civilian and The application of convolutional neural networks (CNNs) military applications, as accurate and timely identification of to automatic modulation classification (AMC) was introduced unknown signals is an essential part of spectrum management by [8]. Their early works [9, 10] together with the release and electronic warfare. The majority of research in this field of several public datasets [11] initiated a wave of interest in has focused on applying deep learning for modulation classification, DL-based RSR. Recently, several alternative DL approaches leaving the task of signal characterisation as an understudied that adopt recurrent neural networks (RNNs) and hybrid architectures area. This paper addresses this gap by presenting [12] were able to consistently achieve above 90% an approach for tackling radar signal classification and characterisation modulation classification accuracy in relatively high signalto-noise as a multi-task learning (MTL) problem. We propose ratio (SNR) settings. Despite the success of DNNs, the IQ Signal Transformer (IQST) among several reference many recent approaches still rely on handcrafted features to architectures that allow for simultaneous optimisation of pre-process the complex-valued, in-phase and quadrature (IQ) multiple regression and classification tasks. We demonstrate data into image-based representations, such as spectrograms the performance of our proposed MTL model on a synthetic [12], prior to training. These approaches effectively transform radar dataset, while also providing a first-of-its-kind benchmark RSR into an image classification problem, and thus limits the for radar signal characterisation.