Miguel Nunes, Hawai?i Space Flight Laboratory, University of Hawai?i at M?noa; Fredrik Bruhn, Unibap AB (publ.), Ma?lardalen University; Robert Wright, Hawaii Institute of Geophysics and Planetology, University of Hawai?i at M?noa; Paul Lucey, Hawaii Institute of Geophysics and Planetology, University of Hawai?i at M?noa; Chiara Ferrari-Wong, Hawaii Institute of Geophysics and Planetology, University of Hawai?i at M?noa; Luke Flynn, Hawai?i Space Flight Laboratory, University of Hawai?i at M?noa; Eric Pilger, Hawai?i Space Flight Laboratory, University of Hawai?i at M?noa; Amber Imai-Hong, Hawai?i Space Flight Laboratory, University of Hawai?i at M?noa; Frances Zhu, Hawai?i Space Flight Laboratory, University of Hawai?i at M?noa; Lance Yoneshige, Hawai?i Space Flight Laboratory, University of Hawai?i at M?noa; Yosef Ben Gershom, Hawai?i Space Flight Laboratory, University of Hawai?i at M?noa; Trevor Sorensen, Hawai?i Space Flight Laboratory, University of Hawai?i at M?noa
Keywords: Hyperspectral Thermal Imaging, Longwave Infrared, CubeSat, Machine Learning, SSA
Abstract:
Hyperspectral Thermal Imagers provide characteristic information that conventional spectral imagers cannot offer. The proliferation of space assets and debris will require eyes in the skies” to track objects effectively. The current estimates as of 2022 state that more than 27,000 pieces of orbital debris are tracked by the Department of Defenses global Space Surveillance Network (SSN). This number is expected to double in the next ten years with 57,000 satellites expected to be launched by 2029. Ground-based assets will not be able to track this vast number of orbital debris, and space-based monitoring capabilities will have to complement the tracking of assets and debris in the years to come. In this work, we present the Hyperspectral Thermal Imager (HyTI) CubeSat design, initially developed for Earth Observation, that can be adapted for Space Situational Awareness (SSA) applications with machine learning algorithms for fast object detection. With new advances in machine learning hardware and software, the categorization of orbital objects can help reveal features such as geometry, thermal signature, size, among others. For example, given the spectral signatures, it is viable to identify plumes of thrusters and unique characteristics of various materials used in different objects.
HyTI is a 6U CubeSat funded by NASAs Earth Science Technology Office (ESTO) InVEST (In-Space Validation of Earth Science Technologies) program. HyTI demonstrates how high spectral and spatial longwave infrared image data can be acquired from a 6U CubeSat platform. The long wave infrared detector uses a push-broom technique for producing accurate spectral and spatial data for moving targets. HyTI will demonstrate advanced on-orbit real-time data processing and the creation of scientific and operational data products. The payload uses a spatially modulated interferometric imaging technique to produce spectro-radiometrically calibrated image cubes, with 25 channels between 8-10.7 microns. The HyTI performance model indicates narrow band NEDTs of Herein we provide an overview of the HyTI design and how it can be adapted for SSA measurements and applications. We expand on the onboard data reduction and object detection approach and provide an overview of the SpaceCloud Framework containerization of mission management and data applications.
Date of Conference: September 27-20, 2022
Track: Space-Based Assets