Refinement and Validation of Radiation Pressure Models for High Area-To-Mass Ratio Space Objects for Improved Characterization, Tracking, and Orbit Prediction

Abstract

The proposed research aims to develop methods to improve detection, tracking, identification, andcharacterization (detect/track/ID/characterize) of Resident Space Objects (RSOs) in the GEOregime. This research will focus on a class of RSOs known as High Area-To-Mass Ratio (HAMR)objects. Current state of the art in detecting, tracking, identifying, and characterizing HAMRobjects is limited due to the dynamic mismodelling of non-gravitational forces acting on theseobjects. The proposed research shall aim to enhance the physical models that govern the behaviorof HAMR objects, specifically the acceleration due to solar and Earth albedo, solar and Earthradiation, and possibly electrostatic charging effects (Lorentz force). Refined models will then beused to estimate features such as mass, shape, and albedo-area of HAMR objects, in an aim tocharacterize them with higher confidence levels. To validate the refined physical models andcharacteristics of HAMR objects, and show improvements in tracking and prediction accuracies,novel filtering techniques shall be employed to estimate the long-term dynamics and trajectoriesof these objects. In essence, every trajectory can be interpreted as a signal encoded withinformation of all perturbing effects. The goal of model validation will be to extract the relevantperturbations from the trajectory signal and do so in a way that can be used to improve trajectorypredictions. To augment the validation, we will assess how well we can predict the flux reflectedfrom the object as compared to measured fluxes.

Document Details

Document Type
DoD Grant Award
Publication Date
May 30, 2018
Source ID
FA95501810351

Entities

People

  • Moriba Jah

Organizations

  • Air Force Office of Scientific Research
  • United States Air Force
  • University of Texas at Austin

Tags

Readers

  • Computational Modeling and Simulation
  • Computer Vision.
  • Data Mining and Knowledge Discovery.

Technology Areas

  • Space
  • Space - Orbital Debris
  • Space - Space Objects