Volume 54, pp. 256-275, 2021.

An augmented wavelet reconstructor for atmospheric tomography

Ronny Ramlau and Bernadett Stadler


Atmospheric tomography, i.e., the reconstruction of the turbulence profile in the atmosphere, is a challenging task for adaptive optics (AO) systems for the next generation of extremely large telescopes. Within the AO community, the solver of first choice is the so-called Matrix Vector Multiplication (MVM) method, which directly applies the (regularized) generalized inverse of the system operator to the data. For small telescopes this approach is feasible, however, for larger systems such as the European Extremely Large Telescope (ELT), the atmospheric tomography problem is considerably more complex, and the computational efficiency becomes an issue. Iterative methods such as the Finite Element Wavelet Hybrid Algorithm (FEWHA) are a promising alternative. FEWHA is a wavelet-based reconstructor that uses the well-known iterative preconditioned conjugate gradient (PCG) method as a solver. The number of floating point operations and the memory usage are decreased significantly by using a matrix-free representation of the forward operator. A crucial indicator for the real-time performance are the number of PCG iterations. In this paper, we propose an augmented version of FEWHA, where the number of iterations is decreased by $50\%$ using an augmented Krylov subspace method. We demonstrate that a parallel implementation of augmented FEWHA allows the fulfilment of the real-time requirements of the ELT.

Full Text (PDF) [970 KB], BibTeX

Key words

adaptive optics, atmospheric tomography, inverse problems, augmented Krylov subspace methods

AMS subject classifications

65R32, 65Y05, 65Y20, 65B99, 85-08, 85-10

Links to the cited ETNA articles

[38]Vol. 13 (2002), pp. 56-80 Zdeněk Strakoš and Petr Tichý: On error estimation in the conjugate gradient method and why it works in finite precision computations

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