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Small changes in introduction

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Markus Bullmann
2019-12-04 09:38:04 +01:00
parent d674842012
commit dcdaa78133
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tex/chapters/1_introduction.tex
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@@ -1,6 +1,6 @@
\section{Introduction (3/4 - 1 Seite)}
\section{Introduction}
\label{sec:intro}
% (3/4 - 1 Seite)
Many indoor positing system (IPS) rely on radio technologies for estimating a pedestrian's position inside a building \cite{Ebner-15}. Probably the most widespread approaches are those based on Received Signal Strength Indication (RSSI) provided by Wi-Fi, as many buildings nowadays offer good infrastructure and as the be located receiver, standard smartphones can be used. These two features make Wi-Fi particularly interesting for the commercial consumer market, e.g. navigating inside an airport or shopping mall.
%Probleme von RSSI
@@ -19,7 +19,7 @@ However, the accuracy depends heavily on the density of the fingerprints and whe
Recording the fingerprints is a time-consuming and tedious process for large buildings and needs to be redone whenever the environment changes significantly.
In contrast, signal strength prediction methods determine signal strengths for arbitrary locations by using an estimation model instead of real measurements.
This makes an offline phase unnecessary and only the positions of the access points have to be known as well as some other model parameters.
This makes an offline phase unnecessary, but the positions of the access points have to be known together with additional model parameters.
However, complex scenarios with many non-line-of-sight (NLOS) transmissions require an equally complex signal strength prediction model, taking into account the characteristics of the environment like the different attenuation coefficients of the building's walls \cite{PathLossPredictionModelsForIndoor, WithoutThePain}.
The determination of such parameters is often only possible with a great deal of effort and material knowledge, especially for older buildings \cite{Fetzer-18}.
Furthermore, to determine a more or less realistic signal prediction for a location in question, intersections checks of each obstacle within the line-of-sight to the AP have to be calculated, which is costly for larger buildings.
@@ -30,7 +30,7 @@ As both, fingerprinting and signal strength prediction, are based on RSSI readin
For example, temporary and unpredictable occurrences such as large crowds are very difficult to compensate, as the signals are highly attenuated by the human body.
Without additional sources like cameras, there can be no indication where and how many people are in an area, what makes it hard to adapt the above discussed methods accordingly.
On paper, there have been promising alternatives to RSSI for some years now.
There have been promising alternatives to RSSI for some years now.
One of them are time-based distance measurements based on techniques like time of arrival (ToA), time difference of arrival (TDoA) or two way ranging (TWR).
As the method names reveal, they are intuitively based on the delay the signal took to travel from the sender to the receiver.
Multiplied by the propagation speed of light results in the distance between the two nodes.
@@ -38,11 +38,13 @@ The propagation speed of the signal depends on the propagation medium and is slo
However, for most indoor environments the signal propagation speed can be assumed to be constant, as the total travel distance in non-air media is usually negligible short compared to the travel distance in air \cite{marcaletti2014filtering}.
For that reason, time-based distance measurements are assumed to be more robust compared to received power measurements, because the propagation path and interaction with the environment is inherent in the measurement.
As mentioned above most of those techniques are only promising on paper, as hardly any of these have ever made it to the end user market, i.e. the Wi-Fi standard.
%As mentioned above most of those techniques are only promising on paper, as hardly any of these have ever made it to the end user market, i.e. the Wi-Fi standard.
While time based techniques sound promising they are hardly adapted on larger scale outside of specialized applications or test setups as they require special hardware.
Therefore, in a smartphone based system they are usually not a viable option as most common devices lack such hardware.
However, this changed with the publication of IEEE 802.11-2016 in 2016, defining the fine timing measurement (FTM) protocol \cite{FTM_IEEE}.
FTM implements the TWR method for standard conform Wi-Fi devices, measuring the round trip time (RTT) based on time differences at the sender and receiver.
The special feature of the method: no synchronized clocks between transmitter and receiver are needed, which is particularly interesting for smartphone based localization.
By definition the responder (e. g. AP) is passive while the FTM initiator (e. g. smartphone) actively requests FTM measurements.
Hence, no synchronized clocks between nodes are needed, which dramatically reduces the complexity of the method and renders a particularly interesting method for smartphone based localization.
By definition the responder (\eg AP) is passive while the FTM initiator (\eg smartphone) actively requests FTM measurements.
The aim of this work is to evaluate how well FTM is suitable for indoor localization and whether the theoretical promises of a higher robustness compared to RSSI measurements are also true in realistic scenarios.
For this, we use three different position estimation methods, namely a multilateration with least square approximation,