Advanced Orbit Prediction for Resident Space Objects through Physics based Learning

Abstract

This project will create performance guaranteed learning strategies, and enhance SSA capabilities with safer, more robust, and higher accuracy orbit predictions. Furthermore, the essential algorithms under development in this research are fundamental in nature. It is expected that the techniques developed in this project will advance the state of the art in learning theory, data science, and machine learning technologies.

Document Details

Document Type
DoD Grant Award
Publication Date
Jan 14, 2022
Source ID
FA95501910401

Entities

People

  • Xiaoli Bai

Organizations

  • Air Force Office of Scientific Research
  • Rutgers University
  • United States Air Force

Tags

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Space
  • Space - Orbital Debris
  • Space - Space Objects