87 lines
4.6 KiB
TeX
Executable File
87 lines
4.6 KiB
TeX
Executable File
\section{Introduction}
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State of the art indoor localization systems use a fusion of multiple
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(smartphone) sensors to infer the pedestrian's current location within a building
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based on a variety of sensor observations.
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%
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Among those, the internal IMU, namely accelerometer and gyroscope, is often
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used as a core component, that provides accurate relative movement information
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like step- and turn-detection. However, this requires the pedestrian's
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initial position to be well known, e.g. using a GPS-fix just before
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entering the building. Additionally, the sensor's error will sum up over
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time \cite{Koeping14}.
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Depending on the used fusion-method, latter can be addressed
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using a movement model for the pedestrian, that prevents unlikely movements
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and locations. However, this will obviously work only to some extent and still
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requires the initial position to be at least vaguely known.
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%
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Thus, indoor localization systems incorporate the knowledge of sensors,
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that provide absolute location information, like \docWIFI{} and
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\docIBeacon{}s. The signal strength of nearby transmitters, received
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by the smartphone, yields a vague information about the distance
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towards it. While the provided accuracy is relatively low,
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it can be stabilized by the IMU and vice versa.
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The downside of this approach is that both, \docWIFI{} and \docIBeacon{}s, require additional prior
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knowledge to work. To infer the probability of the pedestrian currently
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residing at an arbitrary location, the signal strengths received
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by the smartphone are compared with the signal strengths which should be received at this
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location (prior knowledge). As radio frequency (RF) signals are highly dependent
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on the surroundings, those values can change rapidly within meters.
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%
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That is why fingerprinting became popular, where the required prior knowledge
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is gathered by manually scanning each location within the building e.g.
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using cells of $\approx \SI{2}{\meter}$ in size. This usually leads to
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a very high accuracy due to actual measurements of the real situation.
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However, the amount of work for the initial
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setup and the maintenance, when transmitters are changed or renovations take
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place, is very high.
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Setup- and maintenance effort can be prevented by using models to predict
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the signal strengths that should be received at some arbitrary location.
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Depending on the used model, only a few parameters and the locations of the
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transmitters within the building are required. For newer installations
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the latter is often available and tagged within the building's floorplan.
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%As signals are attenuated by the buildings architecture like walls and floors,
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%advanced models additionally include the floorplan within their prediction.
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Obviously, simple models will represent the real signal strengths only
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to some extent, as not all ambient conditions, such as walls, are considered.
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Furthermore, the choice of the model's parameters depends on the actual architecture and \docWIFI{} setup:
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Parameters that work within building A might not work out within building B.
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Thus, a compromise comes to mind: Instead of using several hundreds of fingerprints,
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a few reference measurements used for a model setup might be a valid tradeoff
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between resulting accuracy and necessary setup time.
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Within this work we will focus on simple signal strength prediction models
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that do not incorporate knowledge of nearby walls, but can be used
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for real-time applications on commercial smartphones.
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%
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To mitigate the issues of those signal strength predictors, we propose a new model
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that is a combination of several simple ones. It is more accurate, requires only minor
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additional computations and thus is well suited for use in mobile applications.
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%
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The to-be-expected accuracy (in \decibel{} and \meter{}) of all models is analyzed for various setups ranging from
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just empirical parameters (no setup time when transmitter positions are known) to optimized
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parameters, where no prior knowledge is necessary and a few reference measurements suffice.
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Besides analyzing the \docWIFI{} performance on its own, we will also have
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a closer look at the resulting performance-changes within a fully featured smartphone-based
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indoor localization system using a movement model based on the building's floorplan,
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together with various other sensors and recursive state estimation based on a particle filter.
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%\todo{
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%fokus:\\
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%- wlan parameter + optimierung\\
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%- evaluation der einzel und gesamtergebnisse
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%}
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%\todo{
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%contribution?:\\
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%- neues wifi modell,\\
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%- neues resampling,\\
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%- model param optimierung + eval was es bringt
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%}
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