\section{Related Work}
\label{sec:relatedWork}

%\begin{itemize}
%
%	\item Thema: Wi-FI
%	\item klassiche auf Wi-Fi RSSI verweisen, wo kommt es her und was gibt es da für Lokalisierung
%	\item RTT Verfahren grob erläutern und viel zitieren
%	\item Wi-Fi RTT bisher schwer, gab einige Hacks für Lokalisierung... die zitieren
%	\item FTM im Standard von IEEE -> die 3 wichtigen Paper dazu
%	\item Mit welchem Methoden Schätzen wir für FTM und RSSI eine Position? (Position Estimation) 
%		\subitem Trilateration klassischer Ansatz
%		\subitem Kalman Filter 
%		\subitem Probabilistische methode
%		\subitem Particle Filtering macht bla und blub und ist bla und blieb - Hier: Ganz simples Bewegungsmodell für PF, damit Vergleichbarkeit gegeben bleibt.
%	\item Klassische Lokalisierungssysteme nutzen meist Particle Filter mit RSSI haben Genauigkeiten von xx und FTM verspricht hier besser Genauigkeiten weil... und hat die und die Vorteile.. deswegen soll diese Arbeit den grundlegenden Mehrwert von FTM gegenüber RSSI untersuchen. 
%\end{itemize}
%
%
%\etal{Ibrahim} lays the required groundwork to use the still experimental FTM standard and verifies the general accuracy \cite{ibrahim2018verification}.
%\etal{Yu} present a system using FTM measurements and multisensor multi-pattern-based dead reckoning based on a Unscented Kalman filter sensor fusion \cite{yu2019robust}.
%\etal{Xu} \cite{xu2019locating}.
%
%Compared to the above state of the art our work... 
%\begin{itemize}
%	\item szenario realistischer und nicht nur quadrate die man läuft
%	\item direkter vergleich mit RSSI
%	\item in der praxis erprobte verfahren darauf laufen lassen. 
%\end{itemize}

% complexe filter etc machen eine Aussage schwer



The FTM protocol was introduced in the \ieeWifiFTM standard but it only started recently to get more and more attention in the scientific community.
One of the earliest work was presented by Intel \cite{banin2016wifi} using a Bayesian Filter to reduce the error in the position estimate.
Two years later \etal{Ibrahim} \cite{ibrahim2018verification} gave a comprehensive verification study of the precision and accuracy of FTM with outdoor and indoor measurements in both the \SI{2.4}{GHz} and \SI{5}{GHz} frequency band.
They conclude that FTM is capable to provide meter-level ranging in open space scenarios, but in the presence of multipath effects the accuracy drops to \SI{5}{m} indoors.
Additionally, \etal{Ibrahim} include a detailed description on how to setup FTM on Linux with \intelOld cards in their work which presumable lead to more publications because off the shelf FTM compatible hardware was now available to a broader audience.

\etal{Yu} \cite{yu2019robust} developed a complete indoor positioning system based on a FTM ranging model combined with a robust dead reckoning algorithm.
A Unscented Kalman filter is used to fuse the sensors









Previous work has only focused on verifying the ranging accuracy of FTM and integrating FTM as part of a larger system.
It is not yet known how FTM performs in indoor positioning scenarios.
The application of complex filters and the combination of several sensors makes it hard to reason about the impact of FTM on a indoor positioning system.
In addition, most experiments are limited to rather optimistic test setups, where the results hardly applicable to real world situations with long and relative complex walks.
For these reasons the goal of this work is set to directly evaluate the indoor positioning performance with FTM.
This is archived by intentionally using a simple particle filter system without a complex movement model and without integrating other sensors.
To evaluate the findings we compare the FTM results to RSSI based positioning.












 
