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New spectral positional invariance approach for superresolution of point-type targets embedded in colored noise

机译:一种新的光谱位置不变性方法,可超分辨嵌入彩色噪声中的点型目标

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A new technique is proposed for high-resolution spectral estimation of point-type targets in multi-grade background scenes. All existing spectral estimation methods operating with short data lengths face the contradiction of providing superresolution of the spectral components related to distinct signal sources, and the smoothed reconstruction of the spectral shape of the extended component as a whole. The method addressed here implies considering the spectrum estimation problem under two paradigms: (i) the Prony estimation of the point-type targets; (ii) the nonparametric maximum entropy estimation applied to reconstruct the image of the background scene. By fusing these two methods, we achieve a substantial improvement in resolutions of point-type targets as well as the background spectral characterization.
机译:提出了一种用于多等级背景场景中点型目标的高分辨率光谱估计的新技术。所有现有的以短数据长度操作的频谱估计方法都面临着以下矛盾:提供与不同信号源相关的频谱分量的超分辨率,以及整个扩展分量频谱形状的平滑重构。这里解决的方法意味着在两个范式下考虑频谱估计问题:(i)点型目标的Prony估计; (ii)用于重构背景场景图像的非参数最大熵估计。通过融合这两种方法,我们在点型目标的分辨率以及背景光谱表征方面实现了实质性的改进。

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