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Analysis of Multicomponent Transient Signals Using MUSIC Superresolution Technique

机译:利用音乐超级化技术分析多组分瞬态信号

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The problem of estimating the parameters of transient signals consisting of real decay constants has for long been a subject of study by many researchers. Such signals arise in many problems in Science and Engineering like nuclear magnetic resonance for medical diagnosis, deep-level transient spectroscopy, fluorescence decay analysis, etc. Many techniques have been suggested by researchers to analyse these signals but they often produce mixed results. A new method of analysis using modified MUSIC (multiple signal classification) subspace algorithm is successfully applied to the analysis of this signal. A noisy multiexponential signal is subjected to a preprocessing procedure consisting of Gardeners' transformation and inverse filtering. Modified MUSIC algorithm is then applied to the deconvolved data. The parameters of focus in this paper are the number of components and decay constants. It is shown that with this technique parameter estimates do not significantly change with Signal to Noise Ratio. The superiority of this algorithm over conventional MUSIC algorithm is also shown.
机译:估计由真实衰变常数组成的瞬态信号参数的问题,很长一直是许多研究人员研究的主题。这些信号在许多科学和工程中存在的问题,如核磁共振,如医学诊断,深层瞬态光谱,荧光衰减分析等。研究人员提出了许多技术来分析这些信号,但它们通常会产生混合结果。使用修改的音乐(多信号分类)子空间算法的一种新的分析方法被成功应用于该信号的分析。嘈杂的多因素信号受到园丁转换和逆滤波的预处理程序。然后将修改的音乐算法应用于Deconvolved数据。本文重点参数是组件和衰减常数的数量。结果表明,利用这种技术,参数估计不会随着信噪比而显着改变。还示出了通过传统音乐算法的该算法的优越性。

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