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Random noise SAR based on compressed sensing

机译:基于压缩感知的随机噪声SAR

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Recent theory of compressed sensing (CS) suggested that exact recovery of an unknown sparse signal can be achieved from few measurements with overwhelming probability. In this paper, we combine CS technology with a random noise SAR and proposed the concept of random noise SAR based on CS. The block diagram of the radar system and the collected data processing procedure was presented. Theoretic analysis show that the sensing matrix of the random noise SAR exhibits good restricted isometry property (RIP).When the target scene is sparse or sparse in any basis, the random noise radar based on CS can get high accuracy image by collecting far less amount of echo data than traditional noise radar does. The conclusions are all demonstrated by simulation experiments.
机译:压缩感测(CS)的最新理论表明,可以通过很少的测量以压倒性的概率实现未知稀疏信号的精确恢复。本文将CS技术与随机噪声SAR结合起来,提出了基于CS的随机噪声SAR的概念。给出了雷达系统的框图和收集的数据处理程序。理论分析表明,随机噪声SAR的感知矩阵具有良好的受限等距特性(RIP)。在目标场景稀疏或任意稀疏的情况下,基于CS的随机噪声雷达只需采集很少的量就可以得到高精度的图像。回波数据比传统的噪声雷达要好。仿真实验证明了上述结论。

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