  The ability to accurately evaluate the performance of location determination
systems is crucial for many applications. Typically, the performance of such
systems is obtained by comparing ground truth locations with estimated
locations. However, these ground truth locations are usually obtained by
clicking on a map or using other worldwide available technologies like GPS.
This introduces ground truth errors that are due to the marking process, map
distortions, or inherent GPS inaccuracy.
  In this paper, we present a theoretical framework for analyzing the effect of
ground truth errors on the evaluation of localization systems. Based on that,
we design two algorithms for computing the real algorithmic error from the
validation error and marking/map ground truth errors, respectively. We further
establish bounds on different performance metrics.
  Validation of our theoretical assumptions and analysis using real data
collected in a typical environment shows the ability of our theoretical
framework to correct the estimated error of a localization algorithm in the
presence of ground truth errors. Specifically, our marking error algorithm
matches the real error CDF within 4%, and our map error algorithm provides a
more accurate estimate of the median/tail error by 150%/72% when the map is
shifted by 6m.
