Title: Indoor Localization Using Visible Light Via Fusion Of Multiple Classifiers,
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.