Learning-based mitigation of atmospheric turbulence effects on high-power laser beams in a simulated environment

Juan Coronel; Steevy Cordette; Juan Galvis; Abdellatif Bouchalkha; Guillaume Matras; Amit Dubey; Jawaher Alameri; Chaouki Kasmi

doi:10.1117/12.2594469

1 August 2021 Learning-based mitigation of atmospheric turbulence effects on high-power laser beams in a simulated environment

Juan Coronel, Steevy Cordette, Juan Galvis, Abdellatif Bouchalkha, Guillaume Matras, Amit Dubey, Jawaher Alameri, Chaouki Kasmi

Author Affiliations +

Proceedings Volume 11834, Laser Communication and Propagation through the Atmosphere and Oceans X; 118340E (2021) https://doi.org/10.1117/12.2594469
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States

Abstract

The atmospheric propagation of Near Infrared (NIR) high-power laser beams is affected by the thermal interactions of the electromagnetic beam with the air and the phase perturbations caused by the air inhomogeneities. These interactions lead to inefficient delivery of energy to far field surface. In this work, the implementation of a simulation model integrating thermal distortions induced by the laser beam and the turbulence of atmosphere is presented. Additionally, an iterative learning method is integrated in the simulator to correct the laser beam profile using a deformable mirror. Simulations are realized for a 1.5 kW laser beam at 1064 nm propagating along 150 m propagation path.

Conference Presentation

Citation Download Citation

Juan Coronel, Steevy Cordette, Juan Galvis, Abdellatif Bouchalkha, Guillaume Matras, Amit Dubey, Jawaher Alameri, and Chaouki Kasmi "Learning-based mitigation of atmospheric turbulence effects on high-power laser beams in a simulated environment", Proc. SPIE 11834, Laser Communication and Propagation through the Atmosphere and Oceans X, 118340E (1 August 2021); https://doi.org/10.1117/12.2594469

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