Synthesis of Disparate Optical Imaging Data for Space Domain Awareness

TL;DR

This paper introduces a Bayesian method to integrate optical imaging data from various sources and epochs, improving orbit determination and tracking of space debris while efficiently compressing data.

Contribution

The paper develops a novel Bayesian algorithm that combines disparate optical imaging data, accommodating different noise levels and compressing data without losing statistical information.

Findings

Achieves statistically optimal orbit inferences from combined data

Handles low signal-to-noise ratio imaging effectively

Enables data compression without loss of statistical detail

Abstract

We present a Bayesian algorithm to combine optical imaging of unresolved objects from distinct epochs and observation platforms for orbit determination and tracking. By propagating the non-Gaussian uncertainties we are able to optimally combine imaging of arbitrary signal-to-noise ratios, allowing the integration of data from low-cost sensors. Our Bayesian approach to image characterization also allows large compression of imaging data without loss of statistical information. With a computationally efficient algorithm to combine multiple observation epochs and multiple telescopes, we show statistically optimal orbit inferences.