This paper presents an Interacting Multiple-Model (IMM) estimator based approach to navigation using the Global Positioning System (GPS). The “soft-switching” IMM estimator obtains its estimate as a weighted sum of the individual estimates from a number of parallel filters matched to different motion modes of the platform, e.g., nearly constant velocity and maneuvering. The goal is to obtain the maximum navigation accuracy from an inexpensive and light GPS-based system, without the need for an inertial navigation unit, which would add both cost and weight. In the case of navigation with maneuvering, for example, with accelerations and decelerations, the IMM estimators can substantially improve navigation accuracy during maneuvers as well as during constant velocity motion over a conventional (extended) Kalman Filter (KF), which is, by necessity, a compromise filter. This paper relies on a detailed modeling of GPS and presents the design of a navigation solution using the IMM estimator. Two different IMM estimator designs are presented and a simulated navigation scenario is used for comparison with two baseline KF estimators. Monte Carlo simulations are used to show that the best IMM estimator significantly outperforms the KF, with about 40-50% improvement in RMS position, speed and course errors.