AI Article Synopsis
- The study investigates the use of penalized likelihood estimation for analyzing sparse photon counting data from distributed target lidar systems.
- By adapting the Poisson Total Variation technique, the researchers produce denoised estimates of backscatter photon flux while maintaining high temporal (50 Hz) and range (75 cm) resolutions.
- The proposed method shows better accuracy in signal recovery compared to traditional histogram-based methods, based on both simulated and real-world 2D atmospheric data.
Article Abstract
In this study, we explore the utilization of penalized likelihood estimation for the analysis of sparse photon counting data obtained from distributed target lidar systems. Specifically, we adapt the Poisson Total Variation processing technique to cater to this application. By assuming a Poisson noise model for the photon count observations, our approach yields denoised estimates of backscatter photon flux and related parameters. This facilitates the processing of raw photon counting signals with exceptionally high temporal and range resolutions (demonstrated here to 50 Hz and 75 cm resolutions), including data acquired through time-correlated single photon counting, without significant sacrifice of resolution. Through examination involving both simulated and real-world 2D atmospheric data, our method consistently demonstrates superior accuracy in signal recovery compared to the conventional histogram-based approach commonly employed in distributed target lidar applications.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11074158 | PMC |
| http://dx.doi.org/10.1038/s41598-024-60464-1 | DOI Listing |
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