Abstract
Recent technological advancements enable the cost-effective deployment of constellations of CubeSats. Constellations of radiometer equipped CubeSats have great potential for use in scientific missions for remote sensing objectives including weather tracking and storm imaging, climate change measurement, and atmospheric science, to name a few. While CubeSats provide a solution to challenges in cost, weight, and power, there exist drawbacks which make radiometer calibration more difficult, specifically due to increased sensitivity of the instrument to ambient conditions, resulting from the lack of an adequate thermal control system. To address this problem, a novel, constellation-level calibration framework is being developed called “Adaptive Calibration of CubeSat Radiometer Constellations (ACCURACy)”. ACCURACy clusters radiometers together using instrument-level telemetry data to identify radiometers in similar states, leveraging a relationship between radiometer gain and instrument telemetry data. These clusters are used to identify when radiometers make calibration measurements while in similar states and store these calibration measurements and times in calibration pools to calibrate other radiometers that are in a similar state in the future. This paper discusses the development of ACCURACy including a MATLAB framework and radiometer data simulator, as well as the performance of ACCURACy compared to current state of the art calibration techniques for constellations of CubeSats.
ACCURACy: Adaptive Calibration Of CubesatRadiometer Constellations
Recent technological advancements enable the cost-effective deployment of constellations of CubeSats. Constellations of radiometer equipped CubeSats have great potential for use in scientific missions for remote sensing objectives including weather tracking and storm imaging, climate change measurement, and atmospheric science, to name a few. While CubeSats provide a solution to challenges in cost, weight, and power, there exist drawbacks which make radiometer calibration more difficult, specifically due to increased sensitivity of the instrument to ambient conditions, resulting from the lack of an adequate thermal control system. To address this problem, a novel, constellation-level calibration framework is being developed called “Adaptive Calibration of CubeSat Radiometer Constellations (ACCURACy)”. ACCURACy clusters radiometers together using instrument-level telemetry data to identify radiometers in similar states, leveraging a relationship between radiometer gain and instrument telemetry data. These clusters are used to identify when radiometers make calibration measurements while in similar states and store these calibration measurements and times in calibration pools to calibrate other radiometers that are in a similar state in the future. This paper discusses the development of ACCURACy including a MATLAB framework and radiometer data simulator, as well as the performance of ACCURACy compared to current state of the art calibration techniques for constellations of CubeSats.