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ACCURACy: A Novel Calibration Framework for CubeSat Radiometer Constellations

  • John Bradburn
  • , Mustafa Aksoy
  • , Lennox Apudo
  • , Varvara Vukolov
  • , Henry Ashley
  • , Dylan VanAllen
  • University at Albany

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

As a result of progress in space technology, more scientific missions are benefiting from using CubeSats equipped with radiometers. CubeSat constellations are especially effective in overcoming obstacles in cost, weight, and power. However, these benefits have certain significant downsides, including the difficulty in calibration due to the increased sensitivity of instruments to ambient conditions. Such limitations prevent conventional calibration methods from being reliably applied to CubeSat radiometers. A novel, constellation-level calibration framework called “Adaptive Calibration of CubeSat Radiometer Constellations (ACCURACy)” is being developed to address this issue. ACCURACy, in its current version, uses telemetry data obtained from thermistors in each CubeSat to cluster constellation members into time-adaptive groups of radiometers in similar states. Each radiometer is assigned membership to a cluster and this status is updated as in-orbit measurements shift in the clustering model. This paper introduces the ACCURACy framework, discusses its theoretical background, and presents a MATLAB prototype with performance and uncertainty analyses using synthetic radiometer data in comparison with traditional radiometer calibration methods.

Original languageEnglish
Article number486
JournalRemote Sensing
Volume17
Issue number3
DOIs
StatePublished - Feb 2025

Keywords

  • calibration
  • constellation
  • cubesat
  • machine learning
  • radiometer
  • smallsat

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