  A main challenge in target localization arises from the lack of reliable
distance measures. This issue is especially pronounced in indoor settings due
to the presence of walls, floors, furniture, and other dynamically changing
conditions such as the movement of people and goods, varying temperature, and
airflows. Here, we develop a new computational framework to estimate the
location of a target without the need for reliable distance measures. The
method, which we term Ordinal UNLOC, uses only ordinal data obtained from
comparing the signal strength from anchor pairs at known locations to the
target. Our estimation technique utilizes rank aggregation, function learning
as well as proximity-based unfolding optimization. As a result, it yields
accurate target localization for common transmission models with unknown
parameters and noisy observations that are reminiscent of practical settings.
Our results are validated by both numerical simulations and hardware
experiments.
