  Both constrained and unconstrained optimization problems regularly appear in
recursive tracking problems engineers currently address -- however, constraints
are rarely exploited for these applications. We define the Kalman Filter and
discuss two different approaches to incorporating constraints. Each of these
approaches are first applied to equality constraints and then extended to
inequality constraints. We discuss methods for dealing with nonlinear
constraints and for constraining the state prediction. Finally, some
experiments are provided to indicate the usefulness of such methods.
