62 lines
4.7 KiB
TeX
Executable File
62 lines
4.7 KiB
TeX
Executable File
\section{Related Work}
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Indoor localization based on \docWIFI{} and received signal strength indications (RSSI) dates back to the year
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2000 and the work of Bahl and Padmanabhan \cite{radar}. During an one-time offline-phase, a
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multitude of reference measurements are conducted. During the online-phase, where the pedestrian
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walks along the building, those prior measurements are compared against live readings.
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The pedestrian's location is inferred using the $k$-nearest neighbor(s) based on the Euclidean distance between currently
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received signal strengths and the readings during the offline-phase.
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Inspired by this initial work, Youssef et al. \cite{horus} proposed a more robust, probabilistic
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approach. Their fingerprints were placed every \SI{1.52}{\meter} and estimated by scanning each location
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100 times. The resulting signal strength distribution for each location is hereafter encoded by a histogram.
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The latter can be compared against live measurements to infer its matching-probability. The center
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of mass among the $k$ highest probabilities, including their weight, describes the pedestrian's current location.
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%
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In \cite{ProbabilisticWlan}, a similar approach is used and compared against nearest neighbor, kernel-density-estimation and machine learning.
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Furthermore, they mention potential issues of (temporarily) invisible transmitters and describe a simple heuristic of how to handle such cases.
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Meng et al. \cite{secureAndRobust} further discuss several fingerprinting issues like environmental changes
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after the fingerprints were recorded. They propose an outlier detection based on RANSAC to remove potentially
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distorted measurements and thus improve the matching process.
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Despite a very high accuracy due to real-world comparisons, aforementioned approaches suffer from tremendous setup-
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and maintainance times.
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Using robots instead of human workforce to accurately gather the necessary
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fingerprints might thus be a viable choice \cite{robotFingerprinting}.
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Being cheaper and more accurate, this technique can also
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be combined with SLAM for cases where the floorplan is unavailable.
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Besides using real world measurements via fingerprinting, model predictions can be used to determine
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signal strengths for arbitrary locations. Propagation models are a well established field of research,
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initially used to determine the \docWIFI{}-coverage for new installations.
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While many of them are intended for outdoor and line-of-sight purposes, they are often applied to indoor use-cases as well
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\cite{ANewPathLossPrediction, PredictingRFCoverage, empiricalPathLossModel}.
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The model-based approach presented by Chintalapudi et al. \cite{WithoutThePain} works without any prior knowledge.
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During a setup phase, pedestrians just walk within the building and transmit all observations to a central
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server. Some GPS fixes with well known position (e.g. entering and leaving the building) observed by the pedestrians
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are used as reference points. A genetic optimization algorithm hereafter estimates both, the parameters for a
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signal strength prediction model and the pedestrian's locations during the walk. The estimated parameters
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can be refined using additional walks and may hereafter be used for the indoor localization process.
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Likewise, it is possible to apply a global optimization that also determines a vague floorplan for the building \cite{crowdinside}.
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As described in previous works, signal strength propagation strongly depends on the transmitter's surroundings and thus on the buildings
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architecture.
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%This induces both, the need for more complex prediction models and the need for filtering approaches
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%to limit the impact of potentially erroneous readings.
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%
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Approaches based on timing like TOA and TDOA, as used within the GPS, or methods estimating the signal's angle-of-arrival (AOA)
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are more accurate, and mostly invariant to architectural obstacles \cite{TimeDifferenceOfArrival1, TOAAOA}.
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Especially signal runtimes are unaffected by walls and thus allow for stable distance estimations, if the used components
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support measuring time-delays down to a few picoseconds. This is why those techniques often need special (measurement) hardware
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to estimate parameters like signal-runtime or signal-phase-shifts. Those requirements usually allow only for some use-cases.
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We therefore focus on the RSSI, that is available on each commodity smartphone, and use a
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a simple signal strength prediction model to estimate the most probable location given the phone's observations.
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Furthermore, we propose a new model based on multiple simple ones, which will reduce the prediction error.
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Several strategies to optimize simple models and the resulting accuracies are hereafter evaluated and discussed.
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