Among many techniques for indoor localization, fingerprinting has been shown to provide a higher accuracy compared to the alternative techniques. Fingerprinting techniques require an initial calibration phase during which site surveyors visit virtually every location in the area of interest to manually collect the fingerprint data. However, this process is labour intensive, tedious, and needs to be repeated with any change in the environment. In this work, we propose a technique for enhancing cellular-based indoor localization fingerprinting systems by automatically increasing the spatial density of the reference points. This can be achieved by generating synthetic measurements for virtually all points in the environment to cover inaccessible places.