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Deep Learning Wavefront Sensing from Object Scene for Directed Energy HEL Systems.

Leonardo Herrera¹, Nicholas Messina¹, Brij N Agrawal¹

¹Department of Mechanical and Aerospace Engineering, Naval Postgraduate School, 1 University Circle, Monterey, CA 93943, USA.

Sensors (Basel, Switzerland)

|January 10, 2026

Summary

This summary is machine-generated.

This study introduces a Deep Learning (DL) approach for wavefront sensing in High Energy Laser (HEL) systems, eliminating the need for traditional sensors. The DL model accurately predicts atmospheric turbulence from UAV imagery, even beyond training parameters.

Keywords:

adaptive optics convolutional neural networks deep learning shack-hartmann sensor wavefront error zernike coefficients

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Area of Science:

Optical Engineering
Artificial Intelligence
Atmospheric Physics

Background:

Atmospheric turbulence distorts High Energy Laser (HEL) wavefronts, degrading system performance.
Conventional Adaptive Optics (AO) systems require wavefront sensors and reference beams, increasing complexity and cost.

Purpose of the Study:

To develop a Deep Learning (DL)-based wavefront sensing method that uses only scene imagery.
To eliminate the need for dedicated wavefront sensors and reference beams in HEL systems.

Main Methods:

A DL model was trained to predict Zernike coefficients (wavefront distortions) from aberrated images of a Reaper Unmanned Aerial Vehicle (UAV).
The model was trained on imagery with varying turbulence levels (D/r0) and tested for generalization on unseen turbulence levels and different UAV types (Mongoose).

Main Results:

The DL model trained across multiple turbulence levels outperformed single-level trained models in accuracy.
The model demonstrated strong generalization, accurately predicting turbulence for levels beyond its training range and for Mongoose UAV imagery.

Conclusions:

DL-based wavefront sensing is a viable alternative to traditional AO, offering reduced complexity and cost.
The proposed DL model provides accurate turbulence prediction and robust generalization for HEL systems operating in atmospheric conditions.