  The filtering distribution is a time-evolving probability distribution on the
state of a dynamical system, given noisy observations. We study the large-time
asymptotics of this probability distribution for discrete-time, randomly
initialized signals that evolve according to a deterministic map $\Psi$. The
observations are assumed to comprise a low-dimensional projection of the
signal, given by an operator $P$, subject to additive noise. We address the
question of whether these observations contain sufficient information to
accurately reconstruct the signal. In a general framework, we establish
conditions on $\Psi$ and $P$ under which the filtering distributions
concentrate around the signal in the small-noise, long-time asymptotic regime.
Linear systems, the Lorenz '63 and '96 models, and the Navier Stokes equation
on a two-dimensional torus are within the scope of the theory. Our main
findings come as a by-product of computable bounds, of independent interest,
for suboptimal filters based on new variants of the 3DVAR filtering algorithm.
