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kein plan ey.. intro ist schwer

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toni
2019-11-30 18:58:14 +01:00
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22
tex/chapters/1_introduction.tex
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@@ -30,13 +30,27 @@ 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.
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.
The propagation speed of the signal depends on the propagation medium and is slower in media with higher relative permittivity compared to air, like concrete walls.
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.
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.
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,
we compare the distance measure obtained by a RSSI-based signal strength prediction model with the distances coming directly from Androids RTT API \cite{}.
higher accuracy with more information about the environment.. walls etc
ap positions und physikalisches model wie log distance. modell relys on many parameters, hard to receive. however provides also good results with only a few parameters, not so accurate then fingerprinting but without large setup time..
-> großer auftritt FTM
Wir wollen in dem Paper zeigen wie sich FTM als Sensor für Indoor verhält und kein komplettes IPS vorstellen. Die Arbeit soll ein Gefühl vermitteln, wie sich FTM für IPS einsetzen lässt und wie es sich in unterschiedlichen estimation methoden verhält. wir nutzen ..., ... und particle filtering für die schätzung der aktuellen position des fußgängers. um die performance von ftm möglichst deutlich herrauszustellen wird in dieser arbeit ein sehr einfaches bewegungsmodell, ohne informationen der imu adaptiert.

11
tex/chapters/3_ftm.tex
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@@ -103,12 +103,13 @@ Additionally, the computation of the wall-attenuation factor model requires cost
\subsection{Fine Timing Measurement}
Time-based distance measurements 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.
The propagation speed of the signal depends on the propagation medium and is slower in media with higher relative permittivity compared to air, like concrete walls.
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.
%Time-based distance measurements 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.
%The propagation speed of the signal depends on the propagation medium and is slower in media with higher relative permittivity compared to air, like concrete walls.
%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, time-based distance measurements are intuitively based on the delay the signal took to travel from the sender to the receiver.
A straightforward method to measure the propagation delay of a signal is time of arrival (ToA), where the propagation time of the signal is computed from absolute time values measured at the transmitter and receiver.
This method is used famously in satellite navigation, \eg GPS.
While being precise, ToA requires costly high precision synchronized clocks, which are not suitable for indoor localization.

21
tex/egbib.bib
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@@ -3692,7 +3692,7 @@ DOI = {10.3390/s18124095}
}
@inproceedings{ibrahim2018verification,
title={Verification: Accuracy evaluation of WiFi fine time measurements on an open platform},
title={{Verification: Accuracy evaluation of WiFi fine time measurements on an open platform}},
author={Ibrahim, Mohamed and Liu, Hansi and Jawahar, Minitha and Nguyen, Viet and Gruteser, Marco and Howard, Richard and Yu, Bo and Bai, Fan},
booktitle={Proceedings of the 24th Annual International Conference on Mobile Computing and Networking},
pages={417--427},
@@ -3712,7 +3712,7 @@ DOI = {10.3390/s18124095}
}
@article{xu2019locating,
title={Locating Smartphones Indoors Using Built-In Sensors and Wi-Fi Ranging With an Enhanced Particle Filter},
title={{Locating Smartphones Indoors Using Built-In Sensors and Wi-Fi Ranging With an Enhanced Particle Filter}},
author={Xu, Shihao and Chen, Ruizhi and Yu, Yue and Guo, Guangyi and Huang, Lixiong},
journal={IEEE Access},
volume={7},
@@ -3723,10 +3723,25 @@ DOI = {10.3390/s18124095}
@inproceedings{marcaletti2014filtering,
title={Filtering noisy 802.11 time-of-flight ranging measurements},
title={{Filtering Noisy 802.11 Time-of-Flight Ranging Measurements}},
author={Marcaletti, Andreas and Rea, Maurizio and Giustiniano, Domenico and Lenders, Vincent and Fakhreddine, Aymen},
booktitle={Proceedings of the 10th ACM International on Conference on emerging Networking Experiments and Technologies},
pages={13--20},
year={2014},
organization={ACM}
}
@ARTICLE{FTM_IEEE,
author={E. {Au}},
journal={IEEE Vehicular Technology Magazine},
title={{The Latest Progress on IEEE 802.11mc and IEEE 802.11ai [Standards]}},
year={2016},
volume={11},
number={3},
pages={19-21},
keywords={IEEE standards;radio spectrum management;wireless LAN;working group;IEEE Local Area Network Metropolitan Area Network Standards Committee;IEEE LAN-Metropolitan Area Network Standards Committee;IEEE LMSC;wireless LANs;WLAN;unlicensed spectrum;IEEE 802.11mc;IEEE 802.11ai;frequency 2.4 GHz;frequency 5 GHz;frequency 60 GHz;IEEE 802.11 Standard;Protocols;Throughput;Next generation networking;Wireless LAN;Timing},
doi={10.1109/MVT.2016.2586398},
ISSN={1556-6080},
month={Sep.},}