  We propose a data assimilation algorithm for the 2D Navier-Stokes equations,
based on the Azouani, Olson, and Titi (AOT) algorithm, but applied to the 2D
Navier-Stokes-Voigt equations. Adapting the AOT algorithm to regularized
versions of Navier-Stokes has been done before, but the innovation of this work
is to drive the assimilation equation with observational data, rather than data
from a regularized system. We first prove that this new system is globally
well-posed. Moreover, we prove that for any admissible initial data, the $L^2$
and $H^1$ norms of error are bounded by a constant times a power of the
Voigt-regularization parameter $\alpha>0$, plus a term which decays
exponentially fast in time. In particular, the large-time error goes to zero
algebraically as $\alpha$ goes to zero. Assuming more smoothness on the initial
data and forcing, we also prove similar results for the $H^2$ norm.
