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Results for “INTERFEROMETRY”
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Atomic Test Mass and Atom Interferometry for Inertial Sensing and Gravity Measurements in Space
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High Speed Interferometry for James Webb Space Telescope Testing
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Scheduling Mission Reconfiguration for an Interferometry Synthetic Aperture Radar Using Deep Reinforcement Learning
This paper presents a method to intelligently adapt the baseline of a synthetic aperture radar based on Deep Rein- forcement Learning to help create plans for missions that use formation flight for Earth observation purposes. The main contribution of this paper is the initial results we have found from applying the tool to a toy mission: measuring the ver- tical structure of forests by using a synthetic aperture radar mounted on a formation of 7 satellites orbiting the Earth in a Sun Synchronous Orbit. We have found that with a reward function based on expected science return over time and fuel usage, the Deep Reinforcement Learning planner is able to create plans with positive scientific returns while minimizing fuel usage. We also find that fuel usage and collision avoid- ance planning is better done with traditional methods, as Deep Reinforcement Learning does not converge to optimal solutions.
X- to Ka- Band Cryogenic LNA Module for Very Long Baseline Interferometry
We report a new result of a packaged low noise amplifier (LNA) module with wide bandwidth of 5 to 35 GHz with low noise temperature performance of 10 - 18 K, while operated at 10 K ambient. The LNA used 3-stages of sub-50 nm gate length, 100% indium channel content indium phosphide (InP) high electron mobility transistors (HEMTs). Wideband cryogenic LNAs are important for future radio astronomy observatories. To our knowledge these results represent the lowest noise achieved in a wideband amplifier from 5-35 GHz.