This paper presents a new class of variable-structure algorithms, referred to as expected-mode augmentation (EMA), for multiple-model estimation. In this approach, the original model set is augmented by a variable set of models intended to match the expected value of the unknown true mode. These models are generated adaptively in real time as (globally or locally) probabilistically weighted sums of modal states over the model set. This makes it possible to cover a large continuous mode space by a relatively small number of models at a given accuracy level. Performance of the proposed EMA algorithms is evaluated via a simulated example of a maneuvering target tracking problem.