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5 September 2008 Self-tuning Kalman filter estimation of atmospheric warp
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In our previous work we have demonstrated that the perceived wander of image intensities as seen through the "windows" of each pixel due to atmospheric turbulence can be modelled as a simple oscillator pixel-by-pixel and a linear Kalman filter (KF) can be finetuned to predict to a certain extent short term future deformations. In this paper, we are expanding the Kalman filter into a Hybrid Extended Kalman filter (HEKF) to fine tune itself by relaxing the oscillator parameters at each individual pixel. Results show that HEKF performs significantly better than linear KF.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Murat Tahtali, Andrew Lambert, and Donald Fraser "Self-tuning Kalman filter estimation of atmospheric warp", Proc. SPIE 7076, Image Reconstruction from Incomplete Data V, 70760F (5 September 2008); doi: 10.1117/12.795888;

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