Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events

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https://www.mdpi.com/2624-6120/3/4/47

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https://doi.org/10.3390/signals3040047
 http://hdl.handle.net/11603/26368

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2022-11-02

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journal articles
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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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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.