The Science of Non-Resolved Space Object Signatures for Space Domain Awareness
Abstract
Advancing Space Doman Awareness (SDA) to provide tactical, predictive, and intelligence information on non-resolved space objects (NRSO) will rely on successfully collecting, modelling, simulating and characterizing signatures about targets from multi-optical (multi-hyperspectral, polarization) geographically diverse measurements. This project aims at developing an understanding of the information carried and limitations from NRSO signatures extracted using multi-hyperspectral and polarization remote sensing that leads to knowledge generation and its translation into information exploitation algorithms to infer, classify, predict and diagnose the health of a NRSO for improved SDA beyond what is currently possible with light curves. The technical program is organized around four objectives- 1. Perform optical signature characterization (spectral and polarization) on various spacecraft materials and study their relation to material properties and evaluate effects of the space environment. 2. Develop, test, and validate models and algorithms for monitoring, identification and classification of NRSO from their signatures. 3. Conduct geographically diverse observation campaigns using the USAFA Falcon Telescope Network, and other assets in three modalities (broadband photometry, slitless spectroscopy, and linear polarization) to both support and be informed by SURI team’s research in material characterization, modeling, simulation and algorithm development. 4. Implement a computational environment based on RIT DIRSIGTM that integrates existing and SURI team material characterization and observation results to generate complex image products and signatures of NRSO to support phenomenology understanding and algorithm development. The team effort is led by The University of Texas at El Paso (a Hispanic Serving Institution) in collaboration with the US Air Force Academy, Georgia Tech Research Institute, and Rochester Institute of Technology. The team brings a wealth of capabilities and experience in remote sensing, material science, modeling and simulation, and machine learning, signal and data analytics. The proposed effort leverages investments by AFOSR and existing collaborations with AFRL-RV and AFRL-RD, NASA-JSC, and international collaborators.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 07, 2024
- Source ID
- FA95502310603
Entities
People
- Miguel Velez-Reyes
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Texas at El Paso