Optimization of linear signal processing in photon counting lidar using Poisson thinning

Photon counting lidar signals generally require smoothing to suppress random noise. While the process of reducing the resolution of the profile reduces random errors, it can also create systematic errors due to the smearing of high gradient signals. The balance between random and systematic errors is generally scene dependent and difficult to find, because errors caused by blurring are generally not analytically quantified. In this work, we introduce the use of Poisson thinning, which allows optimal selection of filter parameters for a particular scene based on quantitative evaluation criteria. Implementation of the optimization step is relatively simple and computationally inexpensive for most photon counting lidar processing.

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Related Dataset #1 : NCAR MPD data. Version 1.0

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Copyright 2020 Optical Society of America.


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Author Hayman, Matthew
Stillwell, Robert A.
Spuler, Scott M.
Publisher UCAR/NCAR - Library
Publication Date 2020-09-15T00:00:00
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Topic Category geoscientificInformation
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Metadata Date 2025-07-11T19:15:33.758377
Metadata Record Identifier edu.ucar.opensky::articles:23643
Metadata Language eng; USA
Suggested Citation Hayman, Matthew, Stillwell, Robert A., Spuler, Scott M.. (2020). Optimization of linear signal processing in photon counting lidar using Poisson thinning. UCAR/NCAR - Library. https://n2t.org/ark:/85065/d7br8wff. Accessed 03 August 2025.

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