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, the information content of multiple measurement types is examined 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. 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. 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 is assessed by virtue of the Hellinger Distance metric.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Characterization of Resident Space object States Using Functional Data Analysis


    Additional title:

    J Astronaut Sci


    Contributors:

    Published in:

    Publication date :

    2022-04-01


    Size :

    23 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Characterization of Resident Space Object States Using Functional Data Analysis

    Kelecy, Thomas | British Library Conference Proceedings | 2020


    Resident Space Object Characterization Using Polarized Light Curves

    Dianetti, Andrew D. / Crassidis, John L. | AIAA | 2022


    Resident Space Object Proper Orbital Elements

    Rosengren, Aaron J. / Amato, Davide / Bombardelli, Claudio et al. | TIBKAT | 2019


    A Metric Analysis of IPAS Resident Space Object Detections

    Lane, M. T. / Baldassini, J. F. / Gaposchkin, E. M. | British Library Conference Proceedings | 1995