83 lines
4.1 KiB
TeX
83 lines
4.1 KiB
TeX
\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.
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Depending on the used sensor fusion method, the 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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to each transmitter. 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 such an approach: both sensors 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, one compares the signal strengths received
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by the smartphone with the signal strengths one should receive at this
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location (prior knowledge). As \docWIFI{} 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: 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 with size of \SI{2}{\meter}. While this provides the highest
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possible accuracy due to actual measurements of the real situation,
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one can easily realize the necessary amount of work for both, the initial
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setup and maintenance when transmitters are changed or renovations take
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place.
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To prevent setup- and maintenance effort, models can be used to predict
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the signal strengths one should receive at some arbitrary location.
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Depending on the used model, only a few parameters and the location of the
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transmitter 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 setup
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and parameters that work within building A might not work out within building B.
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Thus, a compromise comes to mind, that a few reference measurements used
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for a viable model setup might be a valid tradeoff between accuracy and
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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 commodity smartphones. The to-be-expected accuracy
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of those models is analyzed for various setups ranging from just empirical
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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
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suffice.
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Despite analyzing the \docWIFI{} performance on its own, we will also have
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a closer look at the to-be-expected performance within a complete indoor
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localization setup using a floorplan-based movement model together with
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various sensors via 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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