  With the growing integration of location based services (LBS) such as GPS in
mobile devices, indoor position systems (IPS) have become an important role for
research. There are several IPS methods such as AOA, TOA, TDOA, which use
trilateration for indoor location estimation but are generally based on
line-of-sight. Other methods rely on classification such as fingerprinting
which uses WLAN indoor signals. This paper re-examines the classical WLAN
fingerprinting accuracy which uses received signal strength (RSS) measurements
by introducing channel estimates for improvements in the classification of
indoor locations. The purpose of this paper is to improve existing
classification algorithms used in fingerprinting by introducing channel
estimates when there are a low number of APs available. The channel impulse
response, or in this case the channel estimation from the receiver, should
characterize a complex indoor area which usually has multipath, thus providing
a unique signature for each location which proves useful for better pattern
recognition. In this experiment, channel estimates are extracted from a
Software-Defined Radio (SDR) environment, thus exploiting the benefits of SDR
from a NI-USRP model and LabVIEW software. Measurements are taken from a known
building, and several scenarios with one and two access points (APs) are used
in this experiment. Also, three granularities in distance between locations are
analyzed. A Support Vector Machine (SVM) is used as the algorithm for pattern
recognition of different locations based on the samples taken from RSS and
channel estimation coefficients.
