This paper presents a new form of the multiple model adaptive estimation algorithm for improved state tracking in systems with unknown system models. The proposed approach differs from existing multiple model methods in the manner in which the covariance and Kalman gains of the individual filters are calculated. By using the fused model estimate, recursions for the actual estimation error covariances are derived which account for the deviation of the hypothesized model from the fused model. Using these covariances to determine the Kalman gain leads to improved tracking estimates through fusion of model and measurement uncertainty. The proposed algorithm has been compared against the standard multiple model adaptive estimation and interacting multiple model algorithms in two simulated examples, resulting in improved, and comparable tracking performance, respectively.