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Event-Based Camera Modeling for Atmospheric Turbulence Prediction.

Dor Mizrahi1,2, Daniel Brisk1, Yogev Mordechai1

  • 1Applied Physics Division, Soreq Nuclear Research Center, Yavne 81800, Israel.

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Summary
This summary is machine-generated.

Passive neuromorphic event cameras can estimate atmospheric turbulence (Cn2) without active transmitters. This technology offers a compact, low-power alternative for real-time atmospheric monitoring.

Keywords:
atmospheric optical turbulenceevent-based vision sensormachine-learning regressionneuromorphic camerapath-integrated C n 2 estimation

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Area of Science:

  • Atmospheric physics and optics
  • Neuromorphic engineering
  • Machine learning applications

Background:

  • Atmospheric turbulence significantly impacts optical systems and long-range imaging.
  • Conventional turbulence measurement tools (scintilometers) are bulky, require active components, and precise alignment.
  • There is a need for passive, compact, and low-power atmospheric monitoring solutions.

Purpose of the Study:

  • To evaluate the efficacy of passive neuromorphic event cameras in estimating atmospheric turbulence.
  • To determine the refractive-index structure parameter (Cn2) using event-stream data.
  • To compare event camera performance against a ground-truth scintillometer.

Main Methods:

  • Field experiment over a 300 m path using an event camera and a CMOS camera, with a scintillometer as ground truth.
  • Extraction of 19 statistical features from event-stream data over varying integration times (2-50 s).
  • Training machine learning regression models (e.g., XGBoost) to predict Cn2 from extracted features.

Main Results:

  • The best model (XGBoost) achieved a high Pearson correlation (0.93) and a mean absolute relative error of 35% across a wide turbulence range (10^-14 to 10^-12 m^-2/3).
  • Accuracy improved with longer integration times and in regions with higher target contrast.
  • Quantified the influence of integration time, target contrast, and feature stability on estimation accuracy in field conditions.

Conclusions:

  • Passive neuromorphic event cameras can reliably estimate atmospheric turbulence (Cn2) without active illumination.
  • This technology presents a viable alternative to traditional scintillometers, enabling compact and low-power real-time atmospheric monitoring.
  • Event-driven sensing demonstrates potential for advanced environmental monitoring applications.