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Enabling Low-Power Radiometers with Machine Learning Calibration

In the future, smart sensors will be designed to extract maximum value information, while minimizing the resources required to acquire, downlink, and process data. Many sensors like radiometers are only able to produce calibrated measurements after reaching steady state. However, waiting until reaching thermal equilibrium to obtain useful data leads to wasted power, excess useless data, and delays in obtaining useful data. Power cycling a radiometer is one way to circumvent this requirement, but leads to other challenges, as turning power off to instrument not only stops data acquisition until it is powered on, but also past power-on until it reaches thermal equilibrium again. This paper introduces a framework which will use machine learning algorithms to enable the calibration of a radiometer during its transient state after power-on and in the presence of power cycling, aiming to further reduce resource utilization.