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Completed TRL 2 (started at 1, targeting 2)
We propose to study the application of compressive sensing (CS) to measure ice elevation using visible/near-infrared imagers. Compressive sensing theory provides a mathematical framework for sampling at less than the Nyquist rate, while retaining the information of the signal. Using this technique, we can significantly reduce data volume and power, enabling instruments with resource and data bandwidth limitations. This could be a game-changing technology for high-rate sampling instruments as well as for SmallSat applications. We will study this technique as applied to the imagers surveying ice topography. However, the results of this study can be easily extended to various other Earth science applications.
By using this novel technology, we can revolutionize the way data is acquired by exploiting sparsity in data sets. CS performs simultaneous data acquisition and compression at the detector front end itself, there by, reducing the need for resources for storing data and then compressing it. CS can potentially significantly reduce data volume, power and enable data bandwidth limiting instruments. This will be a revolutionary technology for SmallSat type instruments, which have limited resources.
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