Competitive Linear Estimation Under Model Uncertainties

We investigate a linear estimation problem under model uncertainties using a competitive algorithm framework under mean square error (MSE) criteria. Here, the performance of a linear estimator is defined relative to the performance of the linear minimum MSE estimator tuned to the underlying unknown system model. We then find the linear estimator that minimizes this relative performance measure, i.e., the regret, for the worst possible system model. Two definitions of regret are given: first as a difference of MSEs and second as a ratio of MSEs. We demonstrate that finding the linear estimators that minimize these regret definitions can be cast as a Semidefinite Programming (SDP) problem and provide numerical examples.