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Trading spectral efficiency for system latency in true-random noise radar network through template-replay diversity

机译:通过模板 - 重播多样性对真正随机噪声雷达网络中系统延迟的交易谱效率

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The Air Force Institute of Technology (AFIT) has developed an experimental multistatic ultrawideband noise radar network (NoNET) to produce highly accurate, highly resolved imagery of a target scene while maintaining a featureless waveform for efficient spectrum utilization. In this work we present the results of an experimental study to assess the effectiveness of a template replay strategy to mitigate the effects of network latency in the radar network while preserving the Low Probability of Intercept (LPI) nature of the random noise (RN) waveform. Based on laboratory prototype measurements and a MATLAB model of two representative play-back schemes we demonstrate up to a 75% increase in processing efficiency while maintaining the ability to operate in a frequency contested environment.
机译:空军技术研究所(AFIT)开发了一个实验多静电超法噪声雷达网络(NonEt),以产生目标场景的高度准确,高度分辨的图像,同时保持无特色波形以获得高效的频谱利用。在这项工作中,我们介绍了一个实验研究的结果,以评估模板重放策略的有效性,以减轻雷达网络中的网络延迟的影响,同时保留随机噪声(RN)波形的低截距(LPI)性质的概率。基于实验室原型测量和两种代表性返回方案的Matlab模型,我们展示了高达75%的加工效率提高,同时保持在频率竞争环境中运行的能力。

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