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

TL;DR

This paper presents an all-optical diffractive neural network approach for real-time piston sensing in sparse aperture systems, enabling rapid and accurate piston measurement without electrical processing.

Contribution

It introduces a novel optical neural network method that directly converts optical field intensity into piston values, bypassing traditional electrical and imaging steps.

Findings

Achieves high accuracy for monochromatic and broadband light

Demonstrates feasibility with simulations of point sources

Enables light-speed piston sensing for sparse aperture systems

Abstract

It is a crucial issue to realize real-time piston correction in the area of sparse aperture imaging. This paper introduces an optical diffractive neural network-based piston sensing method, which can achieve light-speed sensing. By using detectable intensity to represent pistons, the proposed method is capable of converting complex amplitude distribution of the imaging optical field into piston values directly. Differing from the electrical neural network, the way of intensity representation enables the method to obtain the predicted pistons without imaging acquisition and electrical processing process. The simulations demonstrate the feasibility of the method for point source, and high accuracies are achieved for both monochromatic light and broadband light. This method can greatly improve the real-time performance of piston sensing and contribute to the development of the sparse aperture system.