We consider filter design of a linear system with parameter uncertainty. In contrast to the robust Kalman filter which focuses on a worst case analysis, we propose a design methodology based on iteratively solving a tradeoff problem between nominal performance and robustness to the uncertainty. Our proposed filter can be computed online efficiently, is steady-state stable, and is less conservative than the robust filter.