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Optimal Time-Frequency Distribution Selection for LPI Radar Pulse Classification

机译：LPI雷达脉冲分类的最佳时频分布选择

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摘要

The work presented in this paper shows the performance of various time-frequency distributions when gathering ELectronic INTelligence (ELINT) from an electromagnetic environment that contains transmissions from radars operating in a Low Probability of Interception (LPI) mode. A radar device varying waveform parameters on a pulse-by-pulse basis to enhance sensing capabilities and/or to avoid interception warrants a method that can assign a unique Pulse Descriptor Word (PDW) to each pulse detected. The simulations presented here makes use of a Deep Learning classifier that is fed by time-frequency representations of noisy LFM and FMCW pulses that each have unique signal parameters. The performance of the radar pulse classifier is conveyed for multiple time-frequency methods. The results show that the time-frequency representation requirements for accurate PDW generation varies for each signal parameter being estimated whilst also having a dependence on the SNR of the intercepted signal.
机译：本文介绍的工作显示了从电磁环境中收集电子情报（ELINT）时各种时频分布的性能，该电磁环境包含以低拦截概率（LPI）模式运行的雷达的传输。为了逐个脉冲地改变波形参数以增强感测能力和/或避免拦截，雷达设备需要一种可以为每个检测到的脉冲分配唯一的脉冲描述符字（PDW）的方法。此处提供的模拟利用了深度学习分类器，该分类器由嘈杂的LFM和FMCW脉冲的时频表示提供，每个脉冲都有独特的信号参数。雷达脉冲分类器的性能可通过多种时频方法来传达。结果表明，准确的PDW生成的时频表示要求随所估计的每个信号参数而变化，同时还取决于所截获信号的SNR。

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