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It is a crucial issue to realize real-time piston correction in the area ofsparse aperture imaging. This paper introduces an optical diffractive neuralnetwork-based piston sensing method, which can achieve light-speed sensing. Byusing detectable intensity to represent pistons, the proposed method is capableof converting complex amplitude distribution of the imaging optical field intopiston values directly. Differing from the electrical neural network, the wayof intensity representation enables the method to obtain the predicted pistonswithout imaging acquisition and electrical processing process. The simulationsdemonstrate the feasibility of the method for point source, and high accuraciesare achieved for both monochromatic light and broadband light. This method cangreatly improve the real-time performance of piston sensing and contribute tothe development of the sparse aperture system.

Piston sensing for sparse aperture systems via all-optical diffractive neural network