Attitude-based classification of noncooperative bodies for motion characterization and active control detection
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© 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved. The Earth orbital environment is increasingly cluttered with debris and derelict spacecraft. A practical method to categorize bodies based on their rotational motion has implications both for counterintelligence missions and noncooperative proximity operations. Applications include discriminating between passive bodies and those utilizing attitude control, and matching spacecraft motion to a set of candidate motion models. This paper expands on previous work in which a statistical residual test was used to detect active control on a tumbling rigid body. In the current research, the focus is divided between two problem domains: tactical situations, in which an in-situ agent makes a binary classification of an observed target; and strategic situations, in which data are collected and processed offline. For the tactical scenario, a multiplicative Extended Kalman Filter is used to track a target, and classification is based on statistical consistency of the innovations residuals. For the strategic environment, a gatekeeping statistical test is identified as appropriate for testing a hierarchy of candidate models. Numerical experiments are conducted for each scenario. The multiplicative filter shows promise in detecting impulsive target maneuvers, although the approach is dependent on consistent tuning of the filter. Monte Carlo evaluation of the gatekeeping test against three types of motion shows that correct classification is highly dependent on the magnitude of any disturbances and the length of the data window available.
author list (cited authors)
Woodbury, T. D., Ramos, J. H., & Hurtado, J. E.