Uncertainty Quantification for Space Situational Awareness and Traffic Management

Area of Science:

• Space Surveillance and Situational Awareness
• Astrodynamics and Orbital Mechanics
• Data Fusion and Uncertainty Quantification

Background:

• Accurate on-orbit position estimation is critical for space situational awareness (SSA) and collision avoidance.
• Current methods for quantifying position uncertainty may not fully capture the complexities of sensor data and RSO dynamics.
• Evolving SSA architectures demand robust methods for representing and managing space object positional uncertainties.

Purpose of the Study:

• To develop a sensor-orientated approach for generating and quantifying on-orbit position uncertainty for Resident Space Objects (RSO).
• To establish a mathematical framework supporting separation assurance and collision avoidance.
• To represent navigation and tracking errors as uncertainty volumes, detailing size, shape, and orientation.

Main Methods:

• Development of a mathematical framework utilizing least squares formulation.
• Exploitation of real-time navigation measurements and tracking observables.
• Generation of uncertainty volumes to characterize positional errors.

Main Results:

• A validated methodology for on-orbit position uncertainty generation and quantification.
• Demonstration of the method's capability to support separation assurance and collision avoidance.
• Analysis of sensor performance impact on Gaussian assumptions for uncertainty representation.

Conclusions:

• The proposed sensor-orientated approach provides a sound methodology for RSO position uncertainty.
• Accurate uncertainty quantification is vital for the evolution of SSA and Space Traffic Management (STM).
• Implications for cyber-physical architectures and cognitive human-machine systems in SSA were explored.