Data assimilation processes aim to combine measurement data with background models in an optimal way. In anticipation of the availability of global radio occultation (RO) measurements, a computationally practical data assimilation technique for combining RO data with background ionospheric models has been implemented, and simulations have been conducted to assess the utility of the technique. In simulations where tomographic images provide the truth data and the Parameterized Ionospheric Model (PIM) provides the background, a fourfold decrease in the electron density error at 300 km altitude was achieved. A global assimilation simulation has also been conducted using the International Reference Ionosphere as the truth data. For a constellation of eight RO satellites, a factor of four decrease in the vertical total electron content RMS error has been demonstrated. The same simulation also results in a factor of three decrease in the NmF2 RMS error and a halving of the hmF2 RMS error.