  Most existing fingerprints-based indoor localization approaches are based on
some single fingerprints, such as received signal strength (RSS), channel
impulse response (CIR), and signal subspace. However, the localization accuracy
obtained by the single fingerprint approach is rather susceptible to the
changing environment, multi-path, and non-line-of-sight (NLOS) propagation.
Furthermore, building the fingerprints is a very time consuming process. In
this paper, we propose a novel localization framework by Fusing A Group Of
fingerprinTs (FAGOT) via multiple antennas for the indoor environment. We first
build a GrOup Of Fingerprints (GOOF), which includes five different
fingerprints, namely, RSS, covariance matrix, signal subspace, fractional low
order moment, and fourth-order cumulant, which are obtained by different
transformations of the received signals from multiple antennas in the offline
stage. Then, we design a parallel GOOF multiple classifiers based on AdaBoost
(GOOF-AdaBoost) to train each of these fingerprints in parallel as five strong
multiple classifiers. In the online stage, we input the corresponding
transformations of the real measurements into these strong classifiers to
obtain independent decisions. Finally, we propose an efficient combination
fusion algorithm, namely, MUltiple Classifiers mUltiple Samples (MUCUS) fusion
algorithm to improve the accuracy of localization by combining the predictions
of multiple classifiers with different samples. As compared with the single
fingerprint approaches, the prediction probability of our proposed approach is
improved significantly. The process for building fingerprints can also be
reduced drastically. We demonstrate the feasibility and performance of the
proposed algorithm through extensive simulations as well as via real
experimental data using a Universal Software Radio Peripheral (USRP) platform
with four antennas.
