  Linear minimum mean square error (LMMSE) estimation is often ill-conditioned,
suggesting that unconstrained minimization of the mean square error is an
inadequate approach to filter design. To address this, we first develop a
unifying framework for studying constrained LMMSE estimation problems. Using
this framework, we explore an important structural property of constrained
LMMSE filters involving a certain prefilter. Optimality is invariant under
invertible linear transformations of the prefilter. This parameterizes all
optimal filters by equivalence classes of prefilters. We then clarify that
merely constraining the rank of the filter does not suitably address the
problem of ill-conditioning. Instead, we adopt a constraint that explicitly
requires solutions to be well-conditioned in a certain specific sense. We
introduce two well-conditioned filters and show that they converge to the
unconstrained LMMSE filter as their truncation-power loss goes to zero, at the
same rate as the low-rank Wiener filter. We also show extensions to the case of
weighted trace and determinant of the error covariance as objective functions.
Finally, our quantitative results with historical VIX data demonstrate that our
two well-conditioned filters have stable performance while the standard LMMSE
filter deteriorates with increasing condition number.
