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tex/chapters/relatedwork.tex
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\section{Related Work}
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
2000 and the work of Bahl and Padmanabhan \cite{radar}. During a 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.
The pedestrian's location is inferred using the $k$-nearest neighbor(s) based on the Euclidean distance between currently
@@ -21,21 +21,21 @@
distorted measurements and thus improve the matching process.
Despite a very high accuracy due to real-world comparisons, aforementioned approaches suffer from tremendous setup-
and maintainance times.
and maintenance times.
Using robots instead of human workforce to accurately gather the necessary
fingerprints might thus be a viable choice \cite{robotFingerprinting}.
Being cheaper and more accurate, this technique can also
be combined with SLAM for cases where the floorplan is unavailable.
Besides using real world measurements via fingerprinting, model predictions can be used to determine
signal strengths for arbitrary locations. Propagation models are a well established field of research,
Besides using real-world measurements via fingerprinting, model predictions can be used to determine
signal strengths for arbitrary locations. Propagation models are a well-established field of research,
initially used to determine the \docWIFI{}-coverage for new installations.
While many of them are intended for outdoor and line-of-sight purposes, they are often applied to indoor use-cases as well
\cite{ANewPathLossPrediction, PredictingRFCoverage, empiricalPathLossModel}.
The model-based approach presented by Chintalapudi et al. \cite{WithoutThePain} works without any prior knowledge.
During a setup phase, pedestrians just walk within the building and transmit all observations to a central
server. Some GPS fixes with well known position (e.g. entering and leaving the building) observed by the pedestrians
server. Some GPS fixes with well-known position (e.g. entering and leaving the building) observed by the pedestrians
are used as reference points. A genetic optimization algorithm hereafter estimates both, the parameters for a
signal strength prediction model and the pedestrian's locations during the walk. The estimated parameters
can be refined using additional walks and may hereafter be used for the indoor localization process.
@@ -55,7 +55,7 @@
We therefore focus on the 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.
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.