  For linear time-invariant systems with uncertain parameters belonging to a
finite set, we present a purely deterministic approach to multiple-model
estimation and propose an algorithm based on the minimax criterion using
constrained quadratic programming. The estimator tends to learn the dynamics of
the system, and once the uncertain parameters have been sufficiently estimated,
the estimator behaves like a standard Kalman filter.
