Fully Adaptive Atmospheric Sensor on 6U Platform

This proposal presents the advantages of cognitive radar as it relates to atmospheric sensing in general, and then specifically to small satellite design for atmospheric sensing. A cognitive radar system is one that is able to optimize its parameters in real-time based upon past conditions, current conditions as interpreted from the radar return, any external information available, and an internal prediction of the future conditions of the observed environment [1]. Such an approach would improve upon existing systems because it is able to autonomously adapt to changing operational conditions and therefore always operate at maximum efficiency. Atmospheric sensors operate in conditions that can change rapidly ([2],[4]) and therefore would generate data more efficiently if they were able to react to those conditions in real-time. Small satellites, especially those adhering to the CubeSat standard, are required to operate within extremely restrictive power constraints ([5],[7],[8]). In order to advance the state of the art in cognitive radar for remote sensing on small satellites, I propose to demonstrate a fully adaptive radar system that would be constrained under the SWaP requirements of a 6U-CubeSat using a cloud profiling application and a SAR application. This would require refining the current fully adaptive radar (FAR) framework in [1] and applying it to the stated applications. The result would be a multi-application radar system capable of adapting its behavior in real-time as well as the necessary theoretical framework to describe such a system. [1] K. L. Bell, C. J. Baker, G. E. Smith, J. T. Johnson, and M. Rangasy, Cognitive Radar Framework for Target Detection and Tracking, IEEE J. Sel. Top. Signal Process., vol. 9, no. 8, pp. 1427-1439, Dec. 2015. [2] E. Im, S. L. Durden, and S. Tanelli, CloudSat: The Cloud Profiling Radar Mission, 2006 CIE International Conference on Radar, 2006, pp. 1-4. [3] D. Vane and G. L. Stephens, The CloudSat Mission and the A-Train: A Revolutionary Approach to Observing Earth’s Atmosphere, 2008 IEEE Aerospace Conference, 2008, pp. 1-5. [4] A. J. Illingworth et al., The EarthCARE Satellite: The Next Step Forward in Global Measurements of Clouds, Aerosols, Precipitation, and Radiation, Bull. Am. Meteorol. Soc., vol. 96, no. 8, pp. 1311-1332, Aug. 2015. [5] SATHYABAMASAT[Online]. Available: http://www.isac.gov.in/student-satellites/html/sathyabamasat.jsp. [Accessed: 12-Oct-2017]. [6] M. Dabrowski, The ION Cubesat. 2006. 7] W. Blackwell et al. Micro MAS: A First Step Towards a Nanosatellite Constellation for Global Storm Observation, AIAA/USU Conf. Small Satell., Aug. 2013. [8] C. Ball et al., Development of the CubeSat Radiometer Radio Frequency Interference Technology Validation (CubeRRT) Mission System, Fort Worth, Texas, 2017.

This project would improve upon existing systems because it is able to autonomously adapt to changing operational conditions and therefore always operate at maximum efficiency. Atmospheric sensors operate in conditions that can change rapidly and therefore would generate data more efficiently if they were able to react to those conditions in real-time.

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