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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,在真实随机噪声雷达网络中以频谱效率换取系统延迟

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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:空军技术学院(AFIT)开发了一种实验性多静态超宽带噪声雷达网络(NoNET),以生成目标场景的高精度,高分辨率图像,同时保持无特征的波形以有效地利用频谱。在这项工作中,我们提供了一项实验研究的结果,以评估模板重放策略的有效性,以减轻雷达网络中网络延迟的影响,同时保留随机噪声(RN)波形的低拦截概率(LPI)性质。基于实验室原型测量和两个代表性回放方案的MATLAB模型,我们证明了处理效率提高了75%,同时保持了在有频率争议的环境中运行的能力。

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