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On-sky results and performance of low latency centroiding algorithms for adaptive optics implemented in FPGA
Author(s): Fanpeng Kong; Manuel Cegarra Polo; Andrew Lambert
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Paper Abstract

We present a fast and reconfigurable architecture of centroiding for wavefront sensing implemented in FPGA, with a short latency in the order of microseconds, due to the tight integration design of a CMOS image sensor and the FPGA, and the fast algorithm implementation. Data are processed straight away as they arrive, so there is no need to use external storage in a high capacity memory, that will generate inconvenient delays for real time applications. In this architecture data are processed in a pipeline or parallel fashion when convenient, by means of a stream processing with moving filters algorithm (SCoG), or fixed regions in the image sensor typical from a Shack-Hartmann array using the 1st Fourier Coefficient (1FC). In this work timing and performance of both algorithms are presented, as well as on-sky tests when attached to a 12-inch Meade LX200 telescope aiming for bright stars. The results show a latency no longer than 10 microseconds, for a region of 300 pixels per row in the image sensor. These architecture provide a promising solution as a part of an adaptive optics system design for astronomy and space situational awareness applications. Possible applications are astronomy and space situational awareness. We show some preliminary results for on-sky tests using these two centroiding algorithms.

Paper Details

Date Published: 18 September 2018
PDF: 10 pages
Proc. SPIE 10772, Unconventional and Indirect Imaging, Image Reconstruction, and Wavefront Sensing 2018, 107720S (18 September 2018); doi: 10.1117/12.2320084
Show Author Affiliations
Fanpeng Kong, The Univ. of New South Wales (Australia)
Manuel Cegarra Polo, The Univ. of New South Wales (Australia)
Andrew Lambert, The Univ. of New South Wales (Australia)


Published in SPIE Proceedings Vol. 10772:
Unconventional and Indirect Imaging, Image Reconstruction, and Wavefront Sensing 2018
Jean J. Dolne; Philip J. Bones, Editor(s)

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