Three-Degree-of-Freedom Estimation of Agile Space Objects Using Marginalized Particle Filters

Abstract

Several innovations are introduced for space object attitude estimation using light-curve measurements. A radiometric measurement noise model is developed to define the observation uncertainty in terms of optical, environmental, space object, and sensor parameters and is validated using experimental data. Additionally, a correlated process noise model is introduced to represent the angular acceleration dynamics. This model is used to account for the unknown inertia and body torques of agile space objects. This linear dynamics model enables the implementation of marginalized particle filters, affording computationally tractable three-degree-of-freedom Bayesian estimation. The synthesis of these novel approaches enables the estimation of attitude and angular velocity states of maneuvering space objects without a priori knowledge of initial attitude while maintaining computational tractability. Simulated results are presented for the full three-degree-of-freedom agile space object attitude estimation problem.

Cognitive Systems Engineering Applied to Decision Support in Space Situational Awareness

“…The sensor tasking problem is a high-dimensional, multiobjective, mixed-integer, nonlinear optimization problem, and current approaches focus on tractable subproblems (e.g., single objectives, limited target objects, limited sensors). Potential SSA sensor tasking needs include maintaining catalogs of space object state observations (DeMars, Hussein, Frueh, Jah, & Scott Erwin, 2015; Hobson, 2014), detecting maneuvers or other anomalies (Jaunzemis, Mathew, & Holzinger, 2016), and estimating control modes or behavior (Coder, Holzinger, & Linares, 2018; Hart et al, 2016; Holzinger, Wetterer, Luu, Sabol, & Hamada, 2014). These objectives are generally not complementary, especially given limited sensor resources, and the different objectives require different tasking approaches; for instance, characterization (e.g., anomaly detection) prefers many observations of a small subset of the catalog, whereas catalog maintenance prefers a diverse set of observations from as many objects as possible.…”