Indoor Localization by Fusing a Group of Fingerprints Based on Random Forests
This work addresses indoor localization challenges for applications like navigation and tracking, offering an incremental improvement over existing group fingerprint methods by enhancing timeliness and accuracy.
The paper tackles the problem of indoor localization by addressing the susceptibility and time-consuming nature of single fingerprint methods, proposing a framework that fuses a group of fingerprints with random forests to improve accuracy and reduce fingerprint-building burden, achieving better performance in unknown indoor scenarios as demonstrated through simulations and real experiments.
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.