  Evolving Internet-of-Things (IoT) applications often require the use of
sensor-based indoor tracking and positioning, for which the performance is
significantly improved by identifying the type of the surrounding indoor
environment. This identification is of high importance since it leads to higher
localization accuracy. This paper presents a novel method based on a cascaded
two-stage machine learning approach for highly-accurate and robust localization
in indoor environments using adaptive selection and combination of RF features.
In the proposed method, machine learning is first used to identify the type of
the surrounding indoor environment. Then, in the second stage, machine learning
is employed to identify the most appropriate selection and combination of RF
features that yield the highest localization accuracy. Analysis is based on
k-Nearest Neighbor (k-NN) machine learning algorithm applied on a real dataset
generated from practical measurements of the RF signal in realistic indoor
environments. Received Signal Strength, Channel Transfer Function, and
Frequency Coherence Function are the primary RF features being explored and
combined. Numerical investigations demonstrate that prediction based on the
concatenation of primary RF features enhanced significantly as the localization
accuracy improved by at least 50% to more than 70%.
