Finite Set Statistics on Manifolds for Space Object Detection, Tracking, Identification, and Characterization

Abstract

New statistical methods have been developed to improve the treatment of uncertainty in space situational awareness. First, a deeper understanding of the previously developed adapted structural (AST) coordinates has been achieved, and the good performance of these coordinates has been demonstrated for the orbital tracking problem. Second, a new observation-centered nonlinear Kalman filter (OCKF) has been developed. Third, a new Normal: conditional-normal distribution has been developed to describe the uncertainty in the propagated angles-only position, especially after long-term propagation. Using this distribution can lead to improved performance in the association problem. Further, the OCKF and NCN contributions together provide an effective closed-form solution to the filtering problem under long-term propagation, especially with high eccentricity, a setting where standard methods struggle.

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Document Details

Document Type
Technical Report
Publication Date
Nov 08, 2021
Accession Number
AD1154578

Entities

People

  • John T. Kent

Organizations

  • University of Leeds

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Artificial Satellites
  • Computational Science
  • Computer Simulations
  • Coordinate Systems
  • Detection
  • Filters
  • Filtration
  • Gaussian Distributions
  • Identification
  • Kalman Filters
  • Longitude
  • Normal Distribution
  • Resident Space Objects
  • Situational Awareness
  • Space Debris
  • Space Objects
  • Space Situational Awareness

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Space Exploration and Orbital Mechanics.
  • Systems Analysis and Design

Technology Areas

  • Space
  • Space - Space Objects