焦平面电场估计的卡尔曼滤波技术
Kalman filtering techniques for focal plane electric field estimation
摘要：
对于日冕探测器来说，它需要焦平面波前控制技术来继续达到更小的角间距和更高的对比度水平。这些校正算法是迭代的，并且控制方法需要对科学相机处的电场进行估计，这需要拍摄的几乎所有图像用于校正。使这种算法对科学曝光的破坏性最小的最好方法是减少估计场所需的数量。我们展示了一个卡尔曼滤波器估计器，使用先验知识来创建的电场的估计，大大减少了估计图像平面电场，同时稳定对不良的信号噪声（SNR）的抑制所需的曝光次数。除了一个显着减少曝光，我们讨论了这种算法的相对优点，估计方案，不包括以前的状态估计的历史，特别是在估计误差和协方差。最终，滤波器将导致一种自适应算法，该算法可以在实验室中估计物理参数，以便对光学训练中的方差具有鲁棒性。
英文摘要：
For a coronagraph to detect faint exoplanets, it will require focal plane wavefront control techniques to continue reaching smaller angular separations and higher contrast levels. These correction algorithms are iterative and the control methods need an estimate of the electric field at the science camera, which requires nearly all of the images taken for the correction. The best way to make such algorithms the least disruptive to science exposures is to reduce the number required to estimate the field. We demonstrate a Kalman filter estimator that uses prior knowledge to create the estimate of the electric field, dramatically reducing the number of exposures required to estimate the image plane electric field while stabilizing the suppression against poor signal-to-noise (SNR). In addition to a significant reduction in exposures, we discuss the relative merit of this algorithm to estimation schemes that do not incorporate prior state estimate history, particularly in regard to estimate error and covariance. Ultimately the filter will lead to an adaptive algorithm which can estimate physical parameters in the laboratory for robustness to variance in the optical train.
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