The Turing Synthetic Radar Dataset: A dataset for pulse deinterleaving
Abstract
We present the Turing Synthetic Radar Dataset, a comprehensive dataset to serve both as a benchmark for radar pulse deinterleaving research and as an enabler of new research methods. The dataset addresses the critical problem of separating interleaved radar pulses from multiple unknown emitters for electronic warfare applications and signal intelligence. Our dataset contains a total of 6000 pulse trains over two receiver configurations, totalling to almost 3 billion pulses, featuring realistic scenarios with up to 110 emitters and significant parameter space overlap. To encourage dataset adoption and establish standardised evaluation procedures, we have launched an accompanying Turing Deinterleaving Challenge, for which models need to associate pulses in interleaved pulse trains to the correct emitter by clustering and maximising metrics such as the V-measure. The Turing Synthetic Radar Dataset is one of the first publicly available, comprehensively simulated pulse train datasets aimed to facilitate sophisticated model development in the electronic warfare community
Cite
@article{arxiv.2602.03856,
title = {The Turing Synthetic Radar Dataset: A dataset for pulse deinterleaving},
author = {Edward Gunn and Adam Hosford and Robert Jones and Leo Zeitler and Ian Groves and Victoria Nockles},
journal= {arXiv preprint arXiv:2602.03856},
year = {2026}
}
Comments
7 pages 6 figures, submitted to International Radar Symposium 2026