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Accession Number:

AD1151237

Title:

Compensation Through Prediction for Atmospheric Turbulence Effects on Target Imaging and High Energy Laser Beam

Author(s):

• Zhang, Jun H

Author Organization(s):

• Naval Postgraduate School

Report Date:

2021-06-01

Abstract:

Atmospheric turbulence significantly degrades the performance of High Energy Laser (HEL) beams. The three key undesirable effects are: (1) degraded target images used for target tracking; (2) in accurate HEL pointing; and (3) reduction in HEL power during propagation to the target. The current approach for compensating for these turbulence effects uses adaptive optics to measure atmospheric turbulence and compensate the aberration in the optical beam. However, an adaptive optics system has limited performance in strong turbulence and an optical system makes the HEL system more complex. With improvements in Deep Learning algorithms and further development in Artificial Intelligence, we used Deep Learning and Convolutional Neural Networks to predict the atmospheric turbulence and compensate for its negative effects on laser beams. The predicted turbulence can be used for image correction and HEL beam correction using a deformable mirror to reduce turbulence effects during propagation.

Document Type:

Conference:

Journal:

Pages:

63

File Size:

1.77MB

Contracts:

Grants:

Descriptors:

• neural networks
• computer vision
• artificial intelligence
• artificial intelligence software
• convolutional neural networks
• laser beams
• lasers
• machine learning
• computers
• optics
• unmanned aerial vehicles
• computational science
• deep learning
• directed energy weapons
• free electron lasers
• high energy lasers
• light sources

SubjectCategory:

• Electrotechnology and Fluidics

Communities of Interest:

• Autonomy

Distribution Statement:

Approved For Public Release

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