Resident Space Object Feature Identification and Attitude Detection using Hierarchical Mixtures of Experts

Abstract

Space Domain Awareness (SDA) involves detecting, tracking, identifying and characterizing resident space objects (RSOs). This paper proposes an algorithm based on a Hierarchical Mixture of Experts (HME) for identifying RSO features and determining an RSO's attitude profile from astrometric and non-resolved photometric observations. This paper discusses the mathematical background of the HME; the assumptions, test scenarios, and results of processing simulated apparent magnitude and angles data. The results show that the HME is capable of detecting the size, shape, reflectivity, and maneuvers performed by an RSO. The HME is also shown to be capable of identifying and distinguishing between nadir pointing, sun-pointing, and spinning objects even though none of the experts in the HME is directly estimating attitude.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2013
Accession Number
AD1148895

Entities

People

  • Angelica Ceniceros
  • David E. Gaylor
  • Elfego Iii Pinon
  • Jessica T. Anderson

Organizations

  • Emergent Space Technologies (United States)
  • University of Arizona

Tags

Communities of Interest

  • Counter IED
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Satellites
  • Celestial Brightness
  • Change Detection
  • Detection
  • Filters
  • Filtration
  • Ground Stations
  • Kalman Filtering
  • Kalman Filters
  • Materials
  • Measurement
  • Orbits
  • Probability
  • Solar Radiation
  • Space Objects
  • Specular Reflection

Readers

  • Artificial Intelligence
  • Naval Mine Countermeasure Systems Development.
  • Space Exploration and Orbital Mechanics.

Technology Areas

  • Space
  • Space - Space Objects
  • Space - Spacecraft Maneuvers