Simulation of RSO Images for Space Situation Awareness (SSA) Using Parallel Processing

With the rapid increase in resident space objects (RSO), there is a growing demand for their identification and characterization to advance space simulation awareness (SSA) programs. Various AI-based technologies are proposed and demonstrated around the world to effectively and efficiently identify...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-11, Vol.21 (23), p.7868
Main Authors: Clark, Ryan, Fu, Yanchun, Dave, Siddharth, Lee, Regina
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
artificial intelligence (AI)
Design
Efficiency
Feasibility studies
Parallel processing
resident space objects (RSOs)
Sensors
Simulation
Situational awareness
Software
space situational awareness (SSA)
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Summary:With the rapid increase in resident space objects (RSO), there is a growing demand for their identification and characterization to advance space simulation awareness (SSA) programs. Various AI-based technologies are proposed and demonstrated around the world to effectively and efficiently identify RSOs from ground and space-based observations; however, there remains a challenge in AI training due to the lack of labeled datasets for accurate RSO detection. In this paper, we present an overview of the starfield simulator to generate a realistic representation of images from space-borne imagers. In particular, we focus on low-resolution images such as those taken with a commercial-grade star tracker that contains various RSO in starfield images. The accuracy and computational efficiency of the simulator are compared to the commercial simulator, namely STK-EOIR to demonstrate the performance of the simulator. In comparing over 1000 images from the Fast Auroral Imager (FAI) onboard CASSIOPE satellite, the current simulator generates both stars and RSOs with approximately the same accuracy (compared to the real images) as STK-EOIR and, an order of magnitude faster in computational speed by leveraging parallel processing methodologies.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21237868