A physics-based data assimilation model of the ionosphere is under development as the central part of a Department of Defense/Multidisciplinary University Research Initiative (MURI)-funded program called Global Assimilation of Ionospheric Measurements (GAIM). With the significant increase in the number of ionospheric observations that will become available over the next decade, this model will provide a powerful tool toward an improved specification and forecasting of the global ionosphere, with an unprecedented accuracy and reliability. The goal of this effort will be specifications and forecasts on spatial grids that can be global, regional, or local (25 km ¿ 25 km). The specification/forecast will be in the form of three-dimensional electron density distributions from 90 km to geosynchronous altitudes (35,000 km). The main data assimilation in GAIM will be performed by a Kalman filter. In this paper we present a practical method for the implementation of a Kalman filter using a new physics-based ionosphere/plasmasphere model (IPM). This model currently includes 5 ion species (O2+, N2+, NO+, O+, and H+) and covers the low and middle latitudes from 90 km to about 20,000 km altitude. A Kalman filter based on approximations of the state error covariance matrix is developed, employing a reduction of the model dimension and a linearization of the physical model. These approximations lead to a dramatic reduction in the computational requirements. To develop and evaluate the performance of the algorithm, we have used an Observation System Simulation Experiment. In this paper, we will initially present the physics-based IPM used in GAIM and demonstrate its use in the reduced state Kalman filter. Initial results of the filter in the South American sector using synthetic measurements are very encouraging and demonstrate the proper performance of the technique.