Effective space domain awareness (SDA) requires accurate positions and identities of artificial satellites. These measurements–critical to effective decision making in the high risk on orbit environment–are daunting in the deep space geosynchronous (GEO) regime. Here, distance precludes collection of spatially resolved measurements from ground-based telescopes. Neural networks designed for deep space object detection and spectroscopic positive identification have been shown to be effective tools for these mission critical SDA measurements. In this work we demonstrate the potential of slitless field spectroscopy to provide simultaneous object detection and identification of on orbit assets at GEO. Slitless spectrographs expose the reflection physics needed for spectroscopic positive identification without destroying the spatial information used for object detection. Such systems are compact and hardened in comparison to classic spectrographs, and may be deployed to small telescopes. In this work we present a GPU-accelerated simulation environment for the production of realistic synthetic imagery to support generation of large datasets for deep learning. We establish a baseline for simultaneous detection and identification performance by training convolutional neural networks on synthetic datasets created with this tool. This work reduces risk for initial technology development and dataset collection, and provides constraints to the design and development of slitless spectrograph systems for space domain awareness.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.