  In indoor positioning, signal fluctuation is highly location-dependent.
However, signal uncertainty is one critical yet commonly overlooked dimension
of the radio signal to be fingerprinted. This paper reviews the commonly used
Gaussian Processes (GP) for probabilistic positioning and points out the
pitfall of using GP to model signal fingerprint uncertainty. This paper also
proposes Deep Gaussian Processes (DGP) as a more informative alternative to
address the issue. How DGP better measures uncertainty in signal fingerprinting
is evaluated via simulated and realistically collected datasets.
