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This study introduces a penalized likelihood method for analyzing sparse photon counting data from lidar systems. The technique improves signal recovery accuracy and preserves high resolution in atmospheric lidar measurements.

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Area of Science:

  • Atmospheric Science
  • Optical Remote Sensing
  • Signal Processing

Background:

  • Distributed target lidar systems generate sparse photon counting data, often affected by noise.
  • Conventional histogram-based methods can struggle with resolution and accuracy in processing this data.
  • High temporal and range resolution are critical for detailed atmospheric analysis.

Purpose of the Study:

  • To adapt and apply penalized likelihood estimation, specifically the Poisson Total Variation (PTV) technique, for analyzing sparse photon counting data in lidar.
  • To develop a method that accurately estimates backscatter photon flux and related parameters from noisy lidar signals.
  • To enable high-resolution lidar data processing without sacrificing signal quality.

Main Methods:

  • Utilized penalized likelihood estimation with a Poisson noise model for photon count data.
  • Adapted the Poisson Total Variation (PTV) processing technique for lidar applications.
  • Evaluated the method using simulated and real-world 2D atmospheric lidar data.

Main Results:

  • The PTV-based method successfully denoised lidar signals, yielding accurate estimates of backscatter photon flux.
  • Achieved high temporal (50 Hz) and range (75 cm) resolutions in processed lidar data.
  • Demonstrated superior signal recovery accuracy compared to conventional histogram-based approaches.

Conclusions:

  • Penalized likelihood estimation, via the PTV technique, offers a robust method for analyzing sparse photon counting data from lidar.
  • The developed approach enhances accuracy and maintains high resolution in atmospheric lidar measurements.
  • This method provides a significant improvement over existing techniques for distributed target lidar data processing.