Abstract

Abstract In laser-pointing-related applications, when only the centroid of a laser spot is considered, then the position and angular errors of the laser beam are often coupled together. In this study, the decoupling of the position and angular errors is achieved from one single spot image by utilizing a neural network technique. In particular, the successful application of the neural network technique relies on novel experimental procedures, including using an appropriate small-focal-length lens and tilting the detector, to physically enlarge the contrast of different spots. This technique, with the corresponding new system design, may prove to be instructive in the future design of laser-pointing-related systems.

Highlights

  • Accurate laser pointing is crucial for many applications such as free-space communication[1], fusion ignition[2], highpower lasers[3] and robot manipulators[4]

  • The pure angular error in many applications can be obtained with the detector located on the infocus plane

  • The artificial neural network technique can establish the connection between the input and the output of systems by learning from datasets, and has been used in many fields for function approximation and pattern recognition[15, 16]

Read more

Summary

Introduction

The artificial neural network technique can establish the connection between the input and the output of systems by learning from datasets, and has been used in many fields for function approximation and pattern recognition[15, 16]. The datasets for the neural network are obtained by the simulation of a prototype laser-pointing system with a special setup, including a tilted charge-coupled device (CCD) detector with known defocus distance and a small-focal-length lens. This setup is designed for obtaining spot images with more distinct features for neural network analysis, such as higher intensity contrast and the required spot size. The current system supplies a more compact structure and an alternative way to approach high measurement accuracy through data methods, so there may be some advantages in accuracy, reliability and synchronization in laser-pointing measurement

Neural network method for laser-pointing error measurement
Results and discussion of prediction in two directions
Conclusions
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.