# Indoor Localization Using Visible Light Via Fusion Of Multiple Classifiers

## TL;DR

This paper presents a novel indoor localization method using visible light signals and multiple classifiers fusion, significantly improving accuracy and robustness over traditional RSS-based approaches.

## Contribution

It introduces a fusion of multiple machine learning classifiers with robust algorithms for enhanced visible light indoor localization accuracy.

## Key findings

- Achieved mean square positioning error below 5cm in experiments.
- GD-LS fusion method improves localization accuracy by over 93%.
- Proposed algorithms outperform classical RSS-based localization methods.

## Abstract

A multiple classifiers fusion localization technique using received signal strengths (RSSs) of visible light is proposed, in which the proposed system transmits different intensity modulated sinusoidal signals by LEDs and the signals received by a Photo Diode (PD) placed at various grid points. First, we obtain some {\emph{approximate}} received signal strengths (RSSs) fingerprints by capturing the peaks of power spectral density (PSD) of the received signals at each given grid point. Unlike the existing RSSs based algorithms, several representative machine learning approaches are adopted to train multiple classifiers based on these RSSs fingerprints. The multiple classifiers localization estimators outperform the classical RSS-based LED localization approaches in accuracy and robustness. To further improve the localization performance, two robust fusion localization algorithms, namely, grid independent least square (GI-LS) and grid dependent least square (GD-LS), are proposed to combine the outputs of these classifiers. We also use a singular value decomposition (SVD) based LS (LS-SVD) method to mitigate the numerical stability problem when the prediction matrix is singular. Experiments conducted on intensity modulated direct detection (IM/DD) systems have demonstrated the effectiveness of the proposed algorithms. The experimental results show that the probability of having mean square positioning error (MSPE) of less than 5cm achieved by GD-LS is improved by 93.03\% and 93.15\%, respectively, as compared to those by the RSS ratio (RSSR) and RSS matching methods with the FFT length of 2000.

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Source: https://tomesphere.com/paper/1703.02184