Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events
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Author/Creator ORCID
Date
2022-11-02
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Citation of Original Publication
Korde-Patel, Asmita, Richard K. Barry, and Tinoosh Mohsenin. 2022. "Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events" Signals 3, no. 4: 794-806. https://doi.org/10.3390/signals3040047
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Public Domain Mark 1.0
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Abstract
Compressive sensing is a simultaneous data acquisition and compression technique, which
can significantly reduce data bandwidth, data storage volume, and power. We apply this technique
for transient photometric events. In this work, we analyze the effect of noise on the detection of
these events using compressive sensing (CS). We show numerical results on the impact of source
and measurement noise on the reconstruction of transient photometric curves, generated due to
gravitational microlensing events. In our work, we define source noise as background noise, or any
inherent noise present in the sampling region of interest. For our models, measurement noise is
defined as the noise present during data acquisition. These results can be generalized for any transient
photometric CS measurements with source noise and CS data acquisition measurement noise. Our
results show that the CS measurement matrix properties have an effect on CS reconstruction in the
presence of source noise and measurement noise. We provide potential solutions for improving
the performance by tuning some of the properties of the measurement matrices. For source noise
applications, we show that choosing a measurement matrix with low mutual coherence can lower
the amount of error caused due to CS reconstruction. Similarly, for measurement noise addition, we
show that by choosing a lower expected value of the binomial measurement matrix, we can lower the
amount of error due to CS reconstruction.