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arXiv 1002.1957astro-ph.IM
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后相关射频干扰分类方法

Post-correlation radio frequency interference classification methods

A. R. Offringa, A. G. de Bruyn, M. Biehl, S. Zaroubi, G. Bernardi, V. N. Pandey

摘要:

我们描述并比较了几种后相关射频干扰分类方法。随着新的和改进的望远镜的观测数据量的增长,迫切需要完全自动化的,强大的方法来减轻射频干扰。我们研究了几种分类方法,发现对于我们使用的数据集,其中最准确的是SumThreshold方法。这是一种结合现有技术的新方法,包括一种新的阈值处理方法。这种迭代方法通过在时间-频率平面中进行曲面拟合来估计天文信号。在简单的模拟情况下,理论识别准确率为95%,错误概率约为0.1%,该方法在实际中与人眼一样好地发现RFI。此外,它是快速的,健壮的,在执行之前不需要数据模型,并且几乎可以在所有配置中使用其默认参数。该方法已被比较使用模拟数据与其他几种缓解技术,包括一个基于奇异值分解的时间-频率矩阵,并已显示出更好的结果比休息。

英文摘要:

We describe and compare several post-correlation radio frequency interference classification methods. As data sizes of observations grow with new and improved telescopes, the need for completely automated, robust methods for radio frequency interference mitigation is pressing. We investigated several classification methods and find that, for the data sets we used, the most accurate among them is the SumThreshold method. This is a new method formed from a combination of existing techniques, including a new way of thresholding. This iterative method estimates the astronomical signal by carrying out a surface fit in the time-frequency plane. With a theoretical accuracy of 95% recognition and an approximately 0.1% false probability rate in simple simulated cases, the method is in practice as good as the human eye in finding RFI. In addition it is fast, robust, does not need a data model before it can be executed and works in almost all configurations with its default parameters. The method has been compared using simulated data with several other mitigation techniques, including one based upon the singular value decomposition of the time-frequency matrix, and has shown better results than the rest.

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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