Abstract 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.
Highlights A-contrario analysis is applied to optical space object tracking data. Performance is qualitatively demonstrated for proof-of-concept on real data. Flexibility to rate-tracking and sidereal-tracking telescope modes is demonstrated.
A-contrario structural inference for space object detection and tracking
Acta Astronautica ; 200 ; 86-96
2022-07-26
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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