Closely-Spaced Object Classification Using MuyGPyS

TL;DR

This paper demonstrates that probabilistic classification with MuyGPyS effectively distinguishes closely-spaced objects in simulated space domain awareness images, outperforming traditional methods especially in challenging conditions.

Contribution

The study introduces the use of MuyGPyS for CSO classification in SDA, showing its advantages over traditional machine learning in handling data limitations.

Findings

MuyGPyS achieves higher accuracy than traditional ML methods.

Classification performance depends on angular separation and magnitude difference.

Results are validated on realistic simulated SDA images.

Abstract

Accurately detecting rendezvous and proximity operations (RPO) is crucial for understanding how objects are behaving in the space domain. However, detecting closely-spaced objects (CSO) is challenging for ground-based optical space domain awareness (SDA) algorithms as two objects close together along the line-of-sight can appear blended as a single object within the point-spread function (PSF) of the optical system. Traditional machine learning methods can be useful for differentiating between singular objects and closely-spaced objects, but many methods require large training sample sizes or high signal-to-noise conditions. The quality and quantity of realistic data make probabilistic classification methods a superior approach, as they are better suited to handle these data inadequacies. We present CSO classification results using the Gaussian process python package, MuyGPyS, and examine classification accuracy as a function of angular separation and magnitude difference between the simulated satellites. This orbit-independent analysis is done on highly accurate simulated SDA images that emulate realistic ground-based commercial-of-the-shelf (COTS) optical sensor observations of CSOs. We find that MuyGPyS outperforms traditional machine learning methods, especially under more challenging circumstances.