Swingler方法在多分量指数分析中的应用,特别注意非均衡数据

Rahat Hasan, Jonathan B. Scott
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引用次数: 4

摘要

Swingler改进了Gardner的工作,提供了一种优雅的反褶积方法,通过该方法可以在时域数据中解析多个求和的指数分量。然而,这种方法的应用仍然有限,因为有一些微妙的复杂因素使许多使用者望而却步。我们提供了一个教程,并扩展了处理非均衡数据的方法。最后指出了该方法在具有附加噪声的近间隔指数分量情况下的局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Swingler's method for analysis of multicomponent exponentials with special attention to non-equispaced data
Swingler enhanced the work of Gardner to provide an elegant deconvolution method by which multiple summed exponential components might be resolved within time-domain data. Nevertheless, the application of the method remains limited owing to subtle complications that discourage many users. We present a tutorial and extend the approach to handle non-equispaced data. Finally the method's limits are identified in the case of closely-spaced exponential components with added input noise.
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