您一直想知道的关于匹配滤波器的一切（但不敢问）
Everything you always wanted to know about matched filters (but were afraid to ask)
摘要：
在本文中，我们回顾了匹配滤波器（MF）技术的应用及其应用检测弱，确定性，平稳，随机，高斯噪声中的光滑信号。这在天文学中特别适合于探测光谱中的发射线和二维地图中的点源。详细的理论发展已经在许多书中出现（例如Kay 1998; Poor 1994; McNicol 2005; Hippenstiel 2002; Macmillan & Creelma 2005; Wickens 2002; Barkat 2005; Tuzlukov 2001; Levy 2008）。我们的目的是研究一些实际问题，通常被忽略的教科书，甚至在专门的文献，例如，信号的离散化和非高斯噪声的性质的影响。为了实现这一目标，我们以回答特定问题的形式呈现每个项目。相关的数学和它的演示被保持在一个最简单的最低限度，希望在实际应用中的MF的真实性能有一个更好的理解。为了便于形式主义，将为一维信号开发参数。扩展到二维信号是微不足道的，将在专门的章节中突出显示。
英文摘要：
In this paper we review the application of the matched filter (MF) technique and its application to detect weak, deterministic, smooth signals in a stationary, random, Gaussian noise. This is particular suitable in astronomy to detect emission lines in spectra and point-sources in two-dimensional maps. A detailed theoretical development is already available in many books (e.g. Kay 1998; Poor 1994; McNicol 2005; Hippenstiel 2002; Macmillan & Creelma 2005; Wickens 2002; Barkat 2005; Tuzlukov 2001; Levy 2008). Our aim is to examine some practical issues that are typically ignored in textbooks or even in specialized literature as, for example, the effects of the discretization of the signals and the non-Gaussian nature of the noise. To this goal we present each item in the form of answers to specific questions. The relative mathematics and its demonstration are kept to a bare simplest minimum, in the hope of a better understanding of the real performances of the MF in practical applications. For the ease of formalism, arguments will be developed for one-dimensional signals. The extension to the two-dimensional signals is trivial and will be highlighted in dedicated sections.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
