Multi-Fidelity Uncertainty Propagation to Track Maneuvering Spacecraft

Abstract

Maneuvering spacecraft create difficulties in space object tracking and characterization, which is critical to identifying contentious behavior and risks. To maintain safety of operations and \ac{SSA}, we must understand how to infer maneuver capabilities for many targets via a minimal set of data. For the case of low-thrust propulsion systems, maneuvers prove difficult to detect when compared to their impulsive counterparts. Previous research focuses on trackers that maintain custody at the cost of estimating a maneuver (e.g., interacting multiple model filters) or assume perfect data association. The goal of this project is to improve robustness and maintain custody of maneuvering spacecraft in the context of both single- and multiple-target tracking. In space object tracking, the computation cost of the prediction step and the number of predictions is the primary driver of tractability. This work leverages a multi-fidelity propagation of the predicted state probability density function to enable tractable maneuver identification and estimation when considering multiple maneuver hypotheses. Specific objectives of this project focused on enhancing the multi-fidelity uncertainty propagation technique for orbit determination and extending that approach to space object tracking.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Aug 21, 2023
Accession Number
AD1216464

Entities

People

  • Brandon A. Jones

Organizations

  • University of Texas at Austin

Tags

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Computations
  • Data Association
  • Earth Orbits
  • Mechanics
  • Military Research
  • Monte Carlo Method
  • Multiple Targets
  • Multitarget Tracking
  • Propulsion Systems
  • Scientific Research
  • Space Force
  • Space Objects
  • Space Situational Awareness
  • Spacecraft
  • Target Tracking

Readers

  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.
  • Space Exploration and Orbital Mechanics.

Technology Areas

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