  If a dynamic system has active constraints on the state vector and they are
known, then taking them into account during modeling is often advantageous.
Unfortunately, in the constrained discrete-time state-space estimation, the
state equality constraint is defined for a parameter matrix and not on a
parameter vector as commonly found in regression problems. To address this
problem, firstly, we show how to rewrite the state equality constraints as
equality constraints on the state matrices to be estimated. Then, we vectorize
the matricial least squares problem defined for modeling state-space systems
such that any method from the equality-constrained least squares framework may
be employed. Both time-invariant and time-varying cases are considered as well
as the case where the state equality constraint is not exactly known.
