Self-tuning Kalman filter estimation of atmospheric warp

Murat Tahtali; Andrew Lambert; Donald Fraser

doi:10.1117/12.795888

5 September 2008 Self-tuning Kalman filter estimation of atmospheric warp

Murat Tahtali, Andrew Lambert, Donald Fraser

Proceedings Volume 7076, Image Reconstruction from Incomplete Data V; 70760F (2008) https://doi.org/10.1117/12.795888
Event: Optical Engineering + Applications, 2008, San Diego, California, United States

Abstract

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.

Citation Download Citation

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); https://doi.org/10.1117/12.795888

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