  A general theoretical framework for Fingerprinting Localization Algorithms
(FPS), given their popularity, can be utilized for their performance studies.
In this work, after setting up an abstract model for FPS, it is shown that
fingerprinting-based localization problem can be cast as a hypothesis testing
(HT) problem and therefore various results in HT literature can be used to
provide insights, guidelines and performance bounds for general FPS. This
framework results in characterization of scaling limits of localization
reliability in terms of number of measurements and other environmental
parameters. It is suggested that Kullback-Leibler (KL) divergence between
probability distributions of selected feature for fingerprinting at different
locations encapsulates information about both accuracy and latency and can be
used as a central performance metric for studying FPS. Although developed for
an arbitrary fingerprint, the framework is particularly used for studying
simple Received Signal Strength (RSS)- based algorithm. The effect of various
parameters on the performance of fingerprinting algorithms is discussed, which
includes path loss and fading characteristics, number of measurements at each
point, number of anchors and their position, and placement of training points.
Representative simulations and experimentation are used to verify validity of
the theoretical frameworks in realistic setups.
