Fast iterative tomographic wavefront estimation with recursive Toeplitz reconstructor structure for large-scale systems

• Tipo: Artigo em Revista Científica Internacional
• Data: 2018

Autores

Ono, YH (Autor) Outra; Correia, C (Autor) Outra; Conan, R (Autor) Outra; Blanco, L (Autor) Outra; Neichel, B (Autor) Outra; Fusco, T (Autor) Outra

Revista

Título: Journal of the Optical Society of America A: Optics and Image Science, and Vision

Vol. 35

Páginas: 1330-1345

ISSN: 1084-7529

Editora: Optical Society of America

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 5 Citações

Outras Informações

ID Authenticus: P-014-ZC7

DOI: 10.1364/josaa.35.001330

Abstract (EN): Tomographic wavefront reconstruction is the main computational bottleneck to realize real-time correction for turbulence-induced wavefront aberrations in future laser-assisted tomographic adaptive-optics (AO) systems for ground-based giant segmented mirror telescopes because of its unprecedented number of degrees of freedom, N, i.e., the number of measurements from wavefront sensors. In this paper, we provide an efficient implementation of the minimum-mean-square error (MMSE) tomographic wavefront reconstruction, which is mainly useful for some classes of AO systems not requiring multi-conjugation, such as laser-tomographic AO, multi-object AO, and ground-layer AO systems, but is also applicable to multi-conjugate AO systems. This work expands that by Conan [Proc. SPIE 9148, 91480R (2014)] to the multi-wavefront tomographic case using natural and laser guide stars. The new implementation exploits the Toeplitz structure of covariance matrices used in an MMSE reconstructor, which leads to an overall ON log N real-time complexity compared with ON2 of the original implementation using straight vector-matrix multiplication. We show that the Toeplitz-based algorithm leads to 60 nm rms wavefront error improvement for the European Extremely Large Telescope laser-tomography AO system over a well-known sparse-based tomographic reconstruction; however, the number of iterations required for suitable performance is still beyond what a real-time system can accommodate to keep up with the time-varying turbulence.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 16