I have done what you are talking about, although I stuck to 2D signal strength maps within each floor of a building. Also, I didn't infer or guess signal strength as a function of location, I measured it at key points and interpolated in between.
Yes, this technique does add accuracy. In the end we found that the signature method converged faster to identify the location to within a particular room, which was the level of accuracy we cared about.
If you measure points in the environment, you have to be careful about how the data is interpolated. It's not as simple as forming some mesh between points for two reasons. First, the points are going to be at irregular coordinates, wherever someone thinks there is a reason the signal strength might not be as otherwise predicted, or because inaccuracies were found at that location. Second, the inherent radial nature of radio reception means you want to interplate the data in a radial coordinate system.
It's been about 6 years since the last time I dealt with this, and example data files have apparently been purged from my disk since. I did find some test files with artificial data for testing the interpolation and signal strength correction process. Here is one example:
The two small white circles show where the simulated measured points were. Each color band is 1 dB wide, with bluer colors showing lower signal strength. The receiver is in the center. This artificial data set contained two measurements, a low one near the receiver, and a strong one farther away but in the same direction. You can see how the interpolation is polar in nature. This was done by a relaxation algorithm on a polar grid. This data was generated from 50000 iterations of the relaxation algorithm. I don't remember the size of the grid. The result was then remapped to a rectangular grid to use for signal strength correction at run time.