Experimental Comparison of Radon Domain Approaches for Resident Space Object's Parameter Estimation

Area of Science:

• Space Surveillance and Tracking (SST)
• Orbital Mechanics
• Signal Processing

Background:

• The increasing population of space objects poses a significant risk to operational space assets.
• Accurate characterization of resident space objects, particularly their size and rotational period, is crucial for space situational awareness.
• The Inverse Radon Transform (IRT) has emerged as a promising technique for space object characterization.

Purpose of the Study:

• To compare different Inverse Radon Transform (IRT) based approaches for estimating the rotational period of space objects.
• To evaluate the performance of these IRT methods using both simulated and experimental data.
• To identify the most effective IRT techniques for space object rotation period estimation.

Main Methods:

• Application of various Inverse Radon Transform (IRT) algorithms to analyze space object data.
• Utilizing simulated datasets to control variables and assess fundamental performance.
• Employing experimental data from ground-based or space-based observations for real-world validation.

Main Results:

• Quantitative comparison of the accuracy and robustness of different IRT methods.
• Identification of specific IRT approaches that yield more precise rotational period estimations.
• Assessment of the impact of data quality and noise on estimation performance.

Conclusions:

• The Inverse Radon Transform (IRT) is a viable technique for estimating space object rotational periods.
• Specific IRT methodologies demonstrate superior performance in rotational period estimation.
• Further research and validation are needed to optimize IRT application in operational space surveillance.