  Indoor localization based on SIngle Of Fingerprint (SIOF) is rather
susceptible to the changing environment, multipath, and non-line-of-sight
(NLOS) propagation. Building SIOF is also a very time-consuming process.
Recently, we first proposed a GrOup Of Fingerprints (GOOF) to improve the
localization accuracy and reduce the burden of building fingerprints. However,
the main drawback is the timeliness. In this paper, we propose a novel
localization framework by Fusing A Group Of fingerprinTs (FAGOT) based on
random forests. In the offline phase, we first build a GOOF from different
transformations of the received signals of multiple antennas. Then, we design
multiple GOOF strong classifiers based on Random Forests (GOOF-RF) by training
each fingerprint in the GOOF. In the online phase, we input the corresponding
transformations of the real measurements into these strong classifiers to
obtain multiple independent decisions. Finally, we propose a Sliding Window
aIded Mode-based (SWIM) fusion algorithm to balance the localization accuracy
and time. Our proposed approaches can work better in an unknown indoor
scenario. The burden of building fingerprints can also be reduced drastically.
We demonstrate the performance of our algorithms through simulations and real
experimental data using two Universal Software Radio Peripheral (USRP)
platforms.
