基于模板重放分集的真随机噪声雷达网络中频谱效率与系统延迟的权衡

P. Collins, John A. Priestly
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引用次数: 2

摘要

美国空军技术学院(AFIT)开发了一种实验性多静态超宽带噪声雷达网络(NoNET),以产生高精度、高分辨率的目标场景图像,同时保持无特征波形,以实现高效的频谱利用。在这项工作中,我们提出了一项实验研究的结果,以评估模板重播策略的有效性,以减轻雷达网络中网络延迟的影响,同时保持随机噪声(RN)波形的低截获概率(LPI)性质。基于实验室原型测量和两种代表性回放方案的MATLAB模型,我们证明了处理效率提高了75%,同时保持了在频率竞争环境中运行的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Trading spectral efficiency for system latency in true-random noise radar network through template-replay diversity
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.
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