Accuracy: Adaptive Calibration of Cubesat Radiometer Constellations

Recent technological developments have enabled usage of constellations of radiometer carrying CubeSats in scientific remote sensing missions. CubeSats, forming such constellations, on the other hand, bring unique challenges in terms of calibration of their instruments as they are easily impacted by ambient conditions. To address this problem, a constellation level calibration framework called 'Adaptive Calibration of CUbesat RAdiometer Constellations (ACCURACy)' is introduced in this paper. The framework utilizes machine-learning algorithms such as principal component analysis and density based clustering to separate constellation members into time-adaptive groups of similar-state radiometers based on their telemetry data. Within each group, all radiometers will contribute to a calibration data pool with their absolute calibration measurements. Such shared data pools, which include measurements of different calibration targets at different times, will facilitate frequent N>2-point absolute calibration; thus, reduce and quantify calibration errors and uncertainties.