Radio frequency interference (RFI) mitigation remains a major challenge in the search for radio technosignatures. Typical mitigation strategies include a direction-of-origin (DoO) filter, where a signal is classified as RFI if it is detected in multiple directions on the sky. These classifications generally rely on estimates of signal properties, such as frequency and frequency drift rate. Convolutional neural networks (CNNs) offer a promising complement to existing filters because they can be trained to analyze dynamic spectra directly, instead of relying on inferred signal properties. In this work, we compiled several data sets consisting of labeled pairs of images of dynamic spectra, and we designed and trained a CNN that can determine whether or not a signal detected in one scan is also present in another scan. This CNN-based DoO filter outperforms both a baseline 2D correlation model as well as existing DoO filters over a range of metrics, with precision and recall values of 99.15% and 97.81%, respectively. We found that the CNN reduces the number of signals requiring visual inspection after the application of traditional DoO filters by a factor of 6-16 in nominal situations.
翻译:减少无线电频率干扰(RFI)仍然是在搜索无线电技术信号方面的一大挑战。典型的减缓战略包括一个源向(DoO)过滤器,如果信号在天空的多个方向被检测到,则该过滤器被归类为RFI。这些分类一般依赖于对信号属性的估计,如频率和频率漂移率等。进化神经网络对现有过滤器提供了很有希望的补充,因为它们可以接受直接分析动态光谱的培训,而不是依赖推断的信号特性。在这项工作中,我们汇编了若干数据集,其中包括有标签的动态光谱图像配对,我们设计和培训了一台CNN,可以确定在一次扫描中检测到的信号是否也存在于另一次扫描中。这种CNN DoO过滤器不仅超越了基线 2D 相关模型,而且超越了现有在一系列指标上的 DoO 过滤器,精确度和回顾值分别为99.15%和97.81%。我们发现CNN在名义状况下应用传统DoO过滤器后需要视觉检查的信号数量减少了6-16系数。