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tex/chapters/relatedwork.tex
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\section{Related Work}
Indoor localization based on received \docWIFI{} signal strengths (RSSI) dates back to the year
Indoor localization based on \docWIFI{} and received signal strength indications (RSSI) dates back to the year
2000 and the work of Bahl and Padmanabhan \cite{radar}. During an one-time offline-phase, a
multitude of reference measurements are conducted. During the online-phase, where the pedestrian
walks along the building, those prior measurements are compared against live readings.
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%This induces both, the need for more complex prediction models and the need for filtering approaches
%to limit the impact of potentially erroneous readings.
%
Approaches based on timing like TOA and TDOA as used within the GPS or methods estimating the signal's angle-of-arrival (AOA)
Approaches based on timing like TOA and TDOA, as used within the GPS, or methods estimating the signal's angle-of-arrival (AOA)
are more accurate, and mostly invariant to architectural obstacles \cite{TimeDifferenceOfArrival1, TOAAOA}.
However, each of those requires special hardware to work.
%
We therefore focus on the well-known RSSI that is available on each commodity smartphone and use a
a simple signal strength prediction model to estimate the most probable location given the phone's observations.
To reduce the prediction error, we propose a new model based on multiple simple ones.
Several strategies to optimize such a model and the to-be-expected accuracy are hereafter discussed and evaluated.
Especially signal runtimes are unaffected by walls and thus allow for stable distance estimations, if the used components
support measuring time-delays down to a few picoseconds. This is why those techniques often need special (measurement) hardware
to estimate parameters like signal-runtime or signal-phase-shifts. Those requirements usually allow only for some use-cases.
We therefore focus on the RSSI, that is available on each commodity smartphone and uses a
a simple signal strength prediction model to estimate the most probable location given the phone's observations.
Furthermore, we propose a new model based on multiple simple ones, which will reduce the prediction error.
Several strategies to optimize simple models and the resulting accuracies are hereafter evaluated and discussed.