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Dataset for Image Plane Wavefront Sensing and Kalman Filtering for Automated Alignment of Off-axis Aspherics

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Abstract

These files contain data supporting all results reported in Li and Savransky, "Image Plane Wavefront Sensing and Kalman Filtering for Automated Alignment of Off-axis Aspherics". In the paper, we present an automated alignment technique on a double off-axis parabolic mirror system. The data supports image processing, derivation of model-based optimal estimation and control, simulation results and evaluation, system analysis reported in the paper.

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The data contained in the .mat files are shared under an Attribution 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/). The material can be shared and built upon, but attribution to the original authors and a statement of changes made is required. The .m scripts are shared under a MIT license: Copyright 2023 Duan Li Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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Cornell University

Date Issued

2023-05-19

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Keywords

Optical alignment; Wavefront sensing; Kalman filtering

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Government Document

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Attribution 4.0 International

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dataset
software

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