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A-contrario structural inference for space object detection and tracking
Acta Astronautica ( IF 3.5 ) Pub Date : 2022-07-31 , DOI: 10.1016/j.actaastro.2022.07.040
Benjamin G. Feuge-Miller , Moriba K. Jah

Monitoring space assets for safety and sustainability compliance requires active measurement of positional information, commonly done via optical remote sensing in which image-frame coordinates are extracted from a high-noise environment of stellar, atmospheric, and hardware features. This task has been traditionally approached by using a-priori models to differentiate potential Anthropogenic Space Objects from the background noise. Source extraction and track-before-detect methods rely on absolute pixel intensity thresholding and require substantial processing to remove noise (stars, hot pixels, etc.), while machine learning shows promise in reducing processing and improving low-visibility performance but requires context-specific labeled training data. We introduce a new approach based on a-contrario detection, arguing that any space object must be unattributable to noise using a sequence of low-fidelity hypotheses. Through this approach, we aim to relax the dependency on a-priori assumptions of data content and improve performance where high-quality data is sparse, poorly labeled, or challenging to characterize, e.g., in satellite-based applications, and provide detection confidence measures from individual data content to enhance risk evaluation for orbital populations. We present an initial qualitative proof-of-concept for our a-contrario approach using data collected by the ASTRIANet telescope network, showing potentially strong performance on Medium Earth Orbit observations for both rate and sidereal-tracking telescope modes. We also discuss how our approach handles epistemic uncertainties, i.e., a lack of a-priori model information, with implications to Type I and Type II error sources and potential mitigation steps when considering Low Earth Orbit observations with higher tracking noise.



中文翻译:

用于空间物体检测和跟踪的 A-contrario 结构推理

监控空间资产以实现安全和可持续性合规性需要主动测量位置信息,这通常通过光学遥感完成,其中图像帧坐标是从恒星、大气和硬件特征的高噪声环境中提取的。传统上,这项任务是通过使用先验模型来区分潜在的人为空间物体与背景噪声来完成的。源提取和检测前跟踪方法依赖于绝对像素强度阈值,并且需要大量处理来去除噪声(星星、热像素等),而机器学习在减少处理和改善低可见度性能方面显示出前景,但需要上下文特定标记的训练数据。我们引入了一种基于a-contrario detection,认为任何空间物体都必须使用一系列低保真假设不能归因于噪声。通过这种方法,我们的目标是放松对数据内容的先验假设的依赖,并在高质量数据稀疏、标记不佳或难以表征的情况下提高性能,例如在基于卫星的应用中,并提供检测置信度测量从个人数据内容,以加强对轨道人口的风险评估。我们为我们的 a-contrario提供了一个初步的定性概念验证使用由 ASTRIANet 望远镜网络收集的数据的方法,显示了在中地球轨道观测速率和恒星跟踪望远镜模式下的潜在强大性能。我们还讨论了我们的方法如何处理认知不确定性,即缺乏先验模型信息,以及在考虑具有较高跟踪噪声的低地球轨道观测时对 I 型和 II 型误差源和潜在缓解措施的影响。

更新日期:2022-07-31
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