UNCLASSIFIED, PUBLIC RELEASE Phase discontinuity classification from single-aperture irradiance patterns using machine learning. To address this limitation, a shock-tolerant phase reconstruction algorithm was developed. This algorithm identifies the shock-affected information and replaces it with adapted information estimated from heuristic relations between the ratio of the bifurcated irradiance peaks and the induced phase discontinuity. This algorithm was proven to work well over a wide range of flight conditions tested with both simulated and experimental data, however, it lacked versatility due to the aforementioned heuristic relations. In this study we propose the integration of machine learning into the existing algorithm. A Convolutional Neural Network (CNN) was developed to receive a single aperture observation-plane irradiance pattern and estimate the associated phase discontinuity. Irradiance pattern imagery was generated for a wide range of simulated flight conditions including varying levels of background noise. Testing of the fully trained model shows accurate estimation of the phase-discontinuity across all simulated conditions, with mean absolute errors ranging from 0.0365 to 0.0706 radians. This integration of machine learning into the shock-tolerant phase reconstruction algorithm extends the usefulness of SHWFS into flight conditions with shock waves, enabling robust wavefront sensing in transonic, supersonic, and hypersonic environments.
UNCLASSIFIED, PUBLIC RELEASE
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