用遗传算法重建LFM信号中的缺失样本

2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI:10.1109/NEUREL.2018.8587029

M. Brajović, B. Lutovac, M. Daković, L. Stanković

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引用次数: 1

摘要

具有缺失样本的非平稳信号的重建是一个特别具有挑战性的课题。压缩感知(CS)重构要求信号在变换域中具有稀疏性。我们对通常出现在ISAR成像中的线性调频(LFM)信号进行了具有共同啁啾率的CS重建。为此，我们采用了遗传算法(GA)。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Reconstruction of Missing Samples in LFM Signals Using the Genetic Algorithm

The reconstruction of non-stationary signals with missing samples is a particularly challenging topic. The compressed sensing (CS) reconstruction requires that signals exhibit sparsity in a transformation domain. We perform the CS reconstruction of the linear frequency modulated (LFM) signals with a common chirp rate, usually appearing in ISAR imaging. To this aim, we apply the genetic algorithm (GA).

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2018 14th Symposium on Neural Networks and Applications (NEUREL)

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