  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.
