To determine the propagation parameters of high-frequency radio waves, an accurate estimate of the ionosphere is desirable. Estimating the ionosphere, especially during geomagnetic storm times, is strongly dependent on perturbations in the neutral composition. Because of this coupling between the ionosphere and neutral atmospheric chemistry, accurate knowledge of the neutral atmospheric composition is critical in estimating the ionosphere. In the research presented here, a data assimilation system is constructed to optimally estimate the neutral composition, and the necessity for implementing an optimized filtering method, like the Kalman filter, is shown. To demonstrate the data assimilation system, an artificial truth thermosphere is created using a physical model. This thermosphere is sampled according to an instrument and satellite simulation algorithm, creating the measurement data set. Noise is then added to the measurement data, to represent observation errors. Data are assimilated, and noise from this data is reduced using a Kalman filter in combination with a state propagation model. Results show that the error in the estimate can be greatly reduced (usually to <6%), even if the observation errors are large (15%), by using a Kalman filter. Best results are obtained by using a Kalman filter together with an accurate physical model.