  Machine learning (ML) solutions to indoor localization problems have become
popular in recent years due to high positioning accuracy and low cost of
implementation. This paper proposes a novel local nonparametric approach for
solving localizations from high-dimensional Received Signal Strength Indicator
(RSSI) values. Our approach consists of a sequence of classification algorithms
that sequentially narrows down the possible space for location solutions into
smaller neighborhoods. The idea of this sequential classification method is
similar to the decision tree algorithm, but a key difference is our splitting
of the dataset at each node is not based on features of input (i.e. RSSI
values), but some discrete-valued variables generated from the output variable
(i.e. the 3D real-world coordinates). The strength of our localization solution
can be tuned to problem specifics by the appropriate choice of how to
sequentially partition the the space of location into smaller neighborhoods.
Using the publicly available indoor localization dataset UJIIndoorLoc, we
evaluate our proposed method vs. the global ML algorithms for the dataset. The
primary contribution of this paper is to introduce a novel local ML solution
for indoor localization problems.
