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

翻译：我们提出了图灵合成雷达数据集，这是一个综合性数据集，旨在为雷达脉冲去交错研究提供基准，并推动新研究方法的发展。该数据集解决了电子战和信号情报应用中，从多个未知辐射源中分离交织雷达脉冲的关键问题。我们的数据集包含6000个脉冲序列，覆盖两种接收机配置，总计近30亿个脉冲，模拟了多达110个辐射源且参数空间存在显著重叠的现实场景。为促进数据集采用并建立标准化评估流程，我们同步发起了图灵去交错挑战赛，参赛模型需通过聚类方法将交织脉冲序列中的脉冲关联至正确辐射源，并优化V-measure等指标。图灵合成雷达数据集是首批公开可用、全面仿真生成的脉冲序列数据集之一，旨在推动电子战领域复杂模型开发。