Attitude Estimation for Unresolved Agile Space Objects with Shape Model Uncertainty

Abstract

The problem of estimating attitude for actively maneuvering or passively rotating Space Objects (SOs) with unknown mass properties / external torques and uncertain shape models is addressed. To account for agile SO maneuvers, angular rates are simply assumed to be random inputs (e.g., process noise), and model uncertainty is accounted for in a bias state with dynamics derived using rst principles. Bayesian estimation approaches are used to estimate the resulting severely non-Gaussian and multi-modal state distributions. Simulated results are given, conclusions regarding performance are made, and future work is outlined.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2012
Accession Number
ADA568767

Entities

People

  • Andrew Harms
  • Charles J. Wetterer
  • Chris Sabol
  • K. K. Luu
  • Kris Hamada
  • Kyle T. Alfriend
  • Marcus J. Holzinger

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Angular Motion
  • Artificial Satellites
  • Celestial Brightness
  • Dynamics
  • Kalman Filters
  • Measurement
  • Orbits
  • Reflectance
  • Sequential Monte Carlo Methods
  • Space Objects
  • Spacecraft
  • Spacecraft Orbits
  • Surface Properties
  • Uncertainty

Readers

  • Computer Vision.
  • Statistical inference.
  • Structural Dynamics.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • Space
  • Space - Space Objects
  • Space - Spacecraft Maneuvers