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Open AccessArticle

Uncertainty Quantification for Space Situational Awareness and Traffic Management

1
School of Engineering, Bundoora, RMIT University, Bundoora, VIC 3083, Australia
2
Politecnico di Torino – DIMEAS, 10129 Turin, Italy
3
Northrop Grumman Corporation, 1550 W. Nursery Rd, Linthicum Heights, MD 21090, USA
*
Author to whom correspondence should be addressed.
Distribution Statement A: Approved for Public Release; Distribution is Unlimited; #19-1316; Dated 07/31/19.
Sensors 2019, 19(20), 4361; https://doi.org/10.3390/s19204361
Received: 16 July 2019 / Revised: 18 September 2019 / Accepted: 25 September 2019 / Published: 9 October 2019
(This article belongs to the Special Issue Aerospace Sensors and Multisensor Systems)
This paper presents a sensor-orientated approach to on-orbit position uncertainty generation and quantification for both ground-based and space-based surveillance applications. A mathematical framework based on the least squares formulation is developed to exploit real-time navigation measurements and tracking observables to provide a sound methodology that supports separation assurance and collision avoidance among Resident Space Objects (RSO). In line with the envisioned Space Situational Awareness (SSA) evolutions, the method aims to represent the navigation and tracking errors in the form of an uncertainty volume that accurately depicts the size, shape, and orientation. Simulation case studies are then conducted to verify under which sensors performance the method meets Gaussian assumptions, with a greater view to the implications that uncertainty has on the cyber-physical architecture evolutions and Cognitive Human-Machine Systems required for Space Situational Awareness and the development of a comprehensive Space Traffic Management framework. View Full-Text
Keywords: Space Traffic Management; Cyber-Physical Systems; Resident Space Object; Space-Based Surveillance; Radar Performance; Gauss–Helmert Method; Space Situational Awareness; Uncertainty Quantification; Covariance Realism; Cognitive Human-Machine Interaction Space Traffic Management; Cyber-Physical Systems; Resident Space Object; Space-Based Surveillance; Radar Performance; Gauss–Helmert Method; Space Situational Awareness; Uncertainty Quantification; Covariance Realism; Cognitive Human-Machine Interaction
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Hilton, S.; Cairola, F.; Gardi, A.; Sabatini, R.; Pongsakornsathien, N.; Ezer, N. Uncertainty Quantification for Space Situational Awareness and Traffic Management. Sensors 2019, 19, 4361.

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