Thomas Kelecy, L3 Harris; Emily Lambert, L3 Harris; Sufyaan Akram, Applied Space Solutions; John Paffett, Applied Space Solutions
Keywords: Characterization; active; passive; dormant, non-resolved
Abstract:
To date, most characterization techniques (e.g. using photometric light curves) take place using time and frequency domain analyses of data samples generally lacking in the complete information content needed for unambiguous characterization of non-resolved Resident Space Objects (RSOs). In this paper, we examine the information content of multiple measurement types using information theoretic and functional data analysis (FDA) approaches which have shown promise in characterizing the physical and dynamic attributes of space objects from non-resolved observations [Kelecy et al., Probabilistic Analysis of Light Curves, Journal of the Astronautical Sciences, DOI: 10.1007/s40295-018-0130-3, Sept 2018]. With limited data and information it may still be valuable to understand whether the state of an RSO is: (a) active (operational), (b) passive (debris), (c) dormant (a potential threat acting passive), or (4) transitionary between any of 2 of the a-c states. Representative use cases are established and the information content is examined in a probabilistic context for a set of simulated astrometric, photometric, Long Wave Infra-red (LWIR) and Radio Frequency (RF) observations for a diverse set of object shapes, sizes and dynamics representative of states a-d are used to demonstrate the application and value of FDA, data clustering and information theory. The results confirm the value of these approaches by correctly categorizing independent sets of measurements and quantifying the likelihood of a given combination of observation types as being associated with a specific object. The value and information contribution of each observation type to the characterization probability is assessed.
Date of Conference: September 15-18, 2020
Track: Non-Resolved Object Characterization