Computationally efficient, practical implementation of tomographic minimum variance wavefront control using laser and natural guide stars for multi-conjugate adaptive optics

Minimum variance tomographic wavefront reconstruction is described in the literature as a 2-step process consisting of the estimation of dominant layers of distributed turbulence above the telescope, followed by least-squares fitting of this estimate onto deformable mirrors (DMs). The estimation problem is performed from wavefront sensor (WFS) measurements obtained from multiple guide stars. For many science cases, laser guide stars (LGSs) are required to increase sky coverage, but in this case a small number of low-order modes of the LGS WFS measurements are strongly corrupted by the uncertain position of the LGS on the sky and must be projected out from the range space of the turbulence-to-LGS WFS influence matrix. These modes are the global tip and tilt (TT) modes for each LGS WFS and possibly the differential focus modes between LGS WFSs. One possible approach to sense these null modes is to incorporate low-order natural guide star (NGS) WFS measurements in the estimation problem, although this complicates the practical implementation of the reconstruction algorithms. To overcome such complication, a split LGS/NGS wavefront control architecture has recently been proposed for multi-conjugate adaptive optics (MCAO). Such an architecture implements two separate control loops driven independently by the closed-loop LGS and NGS measurements, with distinct wavefront reconstruction algorithms and temporal filters applied to the LGS- and NGS-controlled modes. Control of the NGS modes is now a low dimensional problem, so that the reconstruction algorithm and servo bandwidth can be rapidly updated when the NGS asterism or the atmospheric conditions change. Sample simulation results illustrating the comparative performance of the integrated and split approaches are reviewed. Finally, a split Zernike-based architecture for multi-object adaptive optics (MOAO) is discussed.