  In this paper, we study the localization problem in dense urban settings. In
such environments, Global Navigation Satellite Systems fail to provide good
accuracy due to low likelihood of line-of-sight (LOS) links between the
receiver (Rx) to be located and the satellites, due to the presence of
obstacles like the buildings. Thus, one has to resort to other technologies,
which can reliably operate under non-line-of-sight (NLOS) conditions. Recently,
we proposed a Received Signal Strength (RSS) fingerprint and convolutional
neural network-based algorithm, LocUNet, and demonstrated its state-of-the-art
localization performance with respect to the widely adopted k-nearest neighbors
(kNN) algorithm, and to state-of-the-art time of arrival (ToA) ranging-based
methods. In the current work, we first recognize LocUNet's ability to learn the
underlying prior distribution of the Rx position or Rx and transmitter (Tx)
association preferences from the training data, and attribute its high
performance to these. Conversely, we demonstrate that classical methods based
on probabilistic approach, can greatly benefit from an appropriate
incorporation of such prior information. Our studies also numerically prove
LocUNet's close to optimal performance in many settings, by comparing it with
the theoretically optimal formulations.
