Operating devices on a spacecraft without the power and weight overhead of a warm box offers greater variability in mission objectives but it is an open question how reliably commercial components perform in extreme temperature environments from room to liquid nitrogen. The main issue is that commercial devices have no guarantees of functionality or performance when operated outside their stated design ranges.
The inherent performance benefits of using GaN over silicon in power electronics switching circuits (with respect to faster switching speeds, higher power density, and higher power efficiency) have been discussed previously by Milan et al.; yet, little is shown to support the sonic advantages or disadvantages of using GaN in Class D audio. It is shown by Meneghesso et al. that the dynamic on-state resistance of GaN-HEMTs is related to a higher rate of quantum traps at the gate-drain surface and also in the buffer region.
The overall basic approach is to use piezoelectric MEMS sensors and passive voltage gains from piezoelectrics MEMS transformers to detect the physical assets or RF signals, respectively, followed by a low power integrated circuit (the “nanoWatt classifier”) to do the signal processing to classify the detected signals. For physical asset detection and classification, passive piezoelectric MEMS resonant seismic sensors will be utilized to produce output signals suitable for direct use by the nanoWatt classifier.
A Monolithic, Very Low Power, Programmable Harmonic Discrimination System based on Analog Signal Processing
One of the challenges for a remote sensing platform is obtaining a balance between very low power operation (enabling a long dwell time battery operated sensor) and high-fidelity data processing (typically requiring large amounts of power) for optimized signal detection. Digital Signal Processing (DSP) excels at high performance data processing but can require excessive amounts of power, a requirement not suitable for battery-powered remote sensing applications.