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arXiv 1902.02426astro-ph.IMastro-ph.EP
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机器视觉和深度学习用于无线电SETI信号分类

Machine Vision and Deep Learning for Classification of Radio SETI Signals

G. R. Harp, Jon Richards, Seth Shostak Jill C. Tarter, Graham Mackintosh, Jeffrey D. Scargle, Chris Henze, Bron Nelson, G. A. Cox, S. Egly, S. Vinodababu, J. Voien

摘要:

我们将经典的机器视觉和机器深度学习方法应用于原型信号分类器,以寻找外星智慧。我们的新方法使用测量和模拟无线电信号的二维频谱图,这些信号带有技术起源的印记。这些研究是使用从艾伦望远镜阵列的实时SETI观测中捕获的存档窄带信号数据和一组旨在模拟真实观测信号的数字模拟信号进行的。通过将2D频谱图视为图像,我们表明基于自动视觉分析的高质量参数和非参数分类器可以实现高水平的区分度和准确性,以及低假阳性率。对(真实)存档数据进行了基于垂直和水平图像矩和Huff变换的多种特征提取算法,以模拟特征旋转。最成功的算法使用了两步过程,首先使用旋转,缩放和平移不变仿射变换对图像进行滤波,然后与先前定义的一组标记原型示例进行简单的相关。真实数据通常包含多个信号和信号重影,因此我们使用更简单、更可控的数据集进行非参数评估,该数据集是通过模拟复值电压数据产生的,具有与观察到的原型相似的特性。最成功的非参数分类器采用了广泛的残差(卷积)神经网络的基础上预先存在的分类器,在目前使用的普通照片中的对象检测。这些结果是相关的各种各样的研究领域,已经采用频谱分析从时域天文学的观察地震动物发声分析。

英文摘要:

We apply classical machine vision and machine deep learning methods to prototype signal classifiers for the search for extraterrestrial intelligence. Our novel approach uses two-dimensional spectrograms of measured and simulated radio signals bearing the imprint of a technological origin. The studies are performed using archived narrow-band signal data captured from real-time SETI observations with the Allen Telescope Array and a set of digitally simulated signals designed to mimic real observed signals. By treating the 2D spectrogram as an image, we show that high quality parametric and non-parametric classifiers based on automated visual analysis can achieve high levels of discrimination and accuracy, as well as low false-positive rates. The (real) archived data were subjected to numerous feature-extraction algorithms based on the vertical and horizontal image moments and Huff transforms to simulate feature rotation. The most successful algorithm used a two-step process where the image was first filtered with a rotation, scale and shift-invariant affine transform followed by a simple correlation with a previously defined set of labeled prototype examples. The real data often contained multiple signals and signal ghosts, so we performed our non-parametric evaluation using a simpler and more controlled dataset produced by simulation of complex-valued voltage data with properties similar to the observed prototypes. The most successful non-parametric classifier employed a wide residual (convolutional) neural network based on pre-existing classifiers in current use for object detection in ordinary photographs. These results are relevant to a wide variety of research domains that already employ spectrogram analysis from time-domain astronomy to observations of earthquakes to animal vocalization analysis.

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。

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