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Completed TRL 2 (started at 2, targeting 3)
Microwave radiometry is a powerful tool in remote sensing and is used often in NASA space missions from monitoring Earth’s climate to exploring the chemistry of exoplanets and atmospheres in our solar system. Many if not most microwave radiometers are designed to remain powered during operations. Continuous operation facilitates calibration by reducing the influence of a receiver’s transient response on the sensor’s measurement uncertainty. However, available spacecraft power and/or thermal requirements can lead to the need for power cycling a radiometer. Turning power off to an instrument stops its data acquisition but also leads to a loss of data when the instrument is powered back on until its electronics are sufficiently stable to make a calibrated measurement. Rapid power cycling can also be used to reduce the average power draw of the instrument, while providing near-continuous data to the cost of increased measurement uncertainty. This project aims to develop an intelligent calibration algorithm that draws upon the ability of neural networks to learn and utilize the characteristics of a sensor’s transient response to produce calibrated measurements with minimum uncertainty.
This project aims to develop an intelligent calibration algorithm that draws upon the ability of neural networks to learn and utilize the characteristics of a sensor’s transient response to produce calibrated measurements with minimum uncertainty.
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