Indoor Localization by Fusing a Group of Fingerprints Based on Random Forests

TL;DR

This paper introduces a novel indoor localization framework called FAGOT that fuses multiple fingerprints using random forests, significantly improving accuracy and reducing fingerprint construction time in dynamic indoor environments.

Contribution

The paper proposes a new localization method that fuses multiple fingerprints with random forests and a sliding window fusion, enhancing accuracy and efficiency over existing single fingerprint approaches.

Findings

FAGOT outperforms traditional methods in accuracy.

Reduces fingerprint construction time significantly.

Effective in unknown indoor scenarios.

Abstract

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.