Multi-stage Learning for Radar Pulse Activity Segmentation
Zi Huang Akila Pemasiri Simon Denman Clinton Fookes Terrence Martin
Radio signal recognition is a crucial function in electronic warfare. Precise
identification and localisation of radar pulse activities are required by
electronic warfare systems to produce effective countermeasures. Despite the
importance of these tasks, deep learning-based radar pulse activity recognition
methods have remained largely underexplored. While deep learning for radar
modulation recognition has been explored previously, classification tasks are
generally limited to short and non-interleaved IQ signals, limiting their
applicability to military applications. To address this gap, we introduce an
end-to-end multi-stage learning approach to detect and localise pulse
activities of interleaved radar signals across an extended time horizon. We
propose a simple, yet highly effective multi-stage architecture for
incrementally predicting fine-grained segmentation masks that localise radar
pulse activities across multiple channels. We demonstrate the performance of
our approach against several reference models on a novel radar dataset, while
also providing a first-of-its-kind benchmark for radar pulse activity
segmentation.