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An Offset Parameter Optimization Algorithm for Denoising in Photon Counting Lidar.

Zhuangbin Tan1, Yan Zhang2, Ziwen Sun1

  • 1School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 518107, China.

Entropy (Basel, Switzerland)
|November 27, 2024
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Summary
This summary is machine-generated.

This study introduces an improved photon counting entropy method to reduce noise interference in lidar systems. The new algorithm enhances ranging accuracy, making lidar more reliable in challenging conditions.

Keywords:
MLP networkanti-noise methodlinear conversionoffset parameter optimization methodranging errorsolar background noise

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Area of Science:

  • Photon counting lidar technology
  • Signal processing and noise reduction

Background:

  • Weak lidar signals and strong solar background noise can submerge signals.
  • This noise can cause multiple peaks in photon counting entropy denoising, complicating signal-noise distinction and increasing ranging error.

Purpose of the Study:

  • To propose an improved offset parameter optimization algorithm for photon counting entropy.
  • To effectively eliminate noise-induced peak interference and enhance lidar ranging accuracy.

Main Methods:

  • Introduced solar irradiance prediction using a Multi-Layer Perceptron (MLP) network and least squares linear conversion to estimate solar background noise rate.
  • Developed an offset parameter optimization method to mitigate noise interference.

Main Results:

  • Achieved ranging errors within 5 cm in simulations and 30 cm in experiments.
  • Demonstrated an 81.99% and 73.76% increase in average ranging accuracy compared to standard photon counting entropy denoising.
  • Exhibited superior ranging capability compared to other anti-noise methods.

Conclusions:

  • The proposed algorithm effectively eliminates noise-induced peak interference in photon counting lidar.
  • The method significantly enhances ranging accuracy, offering a robust solution for noisy environments.