FHWS/OTHER2017
Archived
3
0
Fork 0
Code Issues 4 Pull Requests Releases Wiki Activity

current TeX

Browse Source 
switched to acm-large
This commit is contained in:
FrankE
2017-05-02 09:53:32 +02:00
parent 690c400e96
commit 84b6b63791
8 changed files with 652 additions and 190 deletions
Download Patch File Download Diff File Expand all files Collapse all files

64
tex/chapters/abstract.tex
View File

@@ -1,12 +1,58 @@
abstract
system setup kostet oft sehr viel zeit [fingerprinting kostet]
deshalb werden alternativen untersucht:
bekannte AP position mit empirischen parametern
und optimierung durch einige referenzmessungen
\begin{abstract}
floorplan wird für die navigation bzw orientierung des anwenders eh gebraucht
dann kann man ihn auch gleich für ein bewegungsmodell nutzen
Indoor localization and indoor pedestrian navigation is an active field of research
with increasing attention.
%
As of today, many systems will run on commodity smartphones but most of them still rely on
fingerprinting, which demands for high setup- and maintenance-times.
Alternatives, such as simple signal strength prediction models, provide fast setup times,
but often do not provide the accuracy required for use-cases like indoor navigation or
location-based services.
%
While more complex models provide an increased accuracy by including architectural knowledge
about walls and other obstacles, they often require additional computation during runtime and
demand for prior knowledge during setup.
Within this work we will thus focus on simple, easy to set-up models and evaluate their
performance compared to real-world measurements. The evaluation ranges from a fully empiric, instant
setup, given the transmitter locations are well-known, to a highly optimized scenario based
on some reference measurements within the building. Furthermore, we will propose a new
signal strength prediction model as a combination of several simple ones. This tradeoff
increases accuracy with only minor additional computations.
%
All of the optimized models are evaluated within an actual smartphone-based
indoor localization system. This system uses the phone's \docWIFI{}, barometer and IMU
to infer the pedestrian's current location via recursive density estimation based on particle filtering.
We will show that while a \SI{100}{\percent} empiric parameter choice for the model already provides enough
accuracy for many use-cases, a small number of reference measurements is enough to dramatically increase
such a system's performance.
es sollte klar werden, dass es auch darum geht, effizient
auf einem normalen smartphone lauffähig zu sein [passend zum journal]
%system setup kostet oft sehr viel zeit [fingerprinting kostet]
%deshalb werden alternativen untersucht:
%bekannte AP position mit empirischen parametern
%und optimierung durch einige referenzmessungen
%floorplan wird für die navigation bzw orientierung des anwenders eh gebraucht
%dann kann man ihn auch gleich für ein bewegungsmodell nutzen
%es sollte klar werden, dass es auch darum geht, effizient
%auf einem normalen smartphone lauffähig zu sein [passend zum journal]
\end{abstract}
% TODO
\begin{CCSXML}
\end{CCSXML}
%\ccsdesc[500]{Computer systems organization~Embedded systems}
%\ccsdesc[300]{Computer systems organization~Redundancy}
%\ccsdesc{Computer systems organization~Robotics}
%s\ccsdesc[100]{Networks~Network reliability}
\keywords{\docWIFI{}, indoor localization, sensor fusion}

68
tex/chapters/introduction.tex
View File

@@ -1,7 +1,7 @@
\section{Introduction}
State of the art indoor localization systems use a fusion of multiple
(Smartphone) sensors to infer the pedestrian's current location within a building
(smartphone) sensors to infer the pedestrian's current location within a building
based on a variety of sensor observations.
%
Among those, the internal IMU, namely accelerometer and gyroscope, is often
@@ -11,29 +11,29 @@
entering the building. Additionally, the sensor's error will sum up over
time.
Depending on the used sensor fusion method, the latter can be addressed,
Depending on the used fusion-method, the latter can be addressed,
using a movement model for the pedestrian, that prevents unlikely movements
and locations. However, this will obviously work only to some extent and still
requires the initial position to be at least vaguely known.
%
Thus, indoor localization systems incorporate the knowledge of sensors,
that provide absolute location information like \docWIFI{} and
that provide absolute location information, like \docWIFI{} and
\docIBeacon{}s. The signal strength of nearby transmitters, received
by the smartphone, yields a vague information about the distance
to each transmitter. While the provided accuracy is relatively low,
towards it. While the provided accuracy is relatively low,
it can be stabilized by the IMU and vice versa.
The downside of such an approach: both sensors require additional prior
The downside of such an approach: both, \docWIFI{} and \docIBeacon{}s, require additional prior
knowledge to work: To infer the probability of the pedestrian currently
residing at an arbitrary location, one compares the signal strengths received
by the smartphone with the signal strengths one should receive at this
location (prior knowledge). As \docWIFI{} signals are highly dependent
location (prior knowledge). As RF-signals are highly dependent
on the surroundings, those values can change rapidly within meters.
%
That is why fingerprinting became popular: The required prior knowledge
That is why fingerprinting became popular, where the required prior knowledge
is gathered by manually scanning each location within the building e.g.
using cells with size of \SI{2}{\meter}. While this provides the highest
using cells with \SI{2}{\meter} in size. While this provides the highest
possible accuracy due to actual measurements of the real situation,
one can easily realize the necessary amount of work for both, the initial
setup and maintenance when transmitters are changed or renovations take
@@ -48,35 +48,39 @@
%advanced models additionally include the floorplan within their prediction.
Obviously, simple models will represent the real signal strengths only
to some extent, as not all ambient conditions, such as walls, are considered.
Furthermore, the choice of the model's parameters depends on the actual setup
and parameters that work within building A might not work out within building B.
Furthermore, the choice of the model's parameters depends on the actual architecture and \docWIFI{} setup:
Parameters that work within building A might not work out within building B.
Thus, a compromise comes to mind, that a few reference measurements used
for a viable model setup might be a valid tradeoff between accuracy and
setup time.
Thus, a compromise comes to mind: Instead of using hundreds of fingerprints,
a few reference measurements used for a model setup might be a valid tradeoff
between resulting accuracy and necessary setup time.
Within this work we will focus on simple signal strength prediction models
that do not incorporate knowledge of nearby walls, but can be used
for real-time applications on commodity smartphones. The to-be-expected accuracy
of those models is analyzed for various setups ranging from just empirical
parameters (no setup time when transmitter positions are known) to optimized
parameters where no prior knowledge is necessary and a few reference measurements
suffice.
for real-time applications on commodity smartphones.
%
To mitigate the issues of those signal strength predictors, we propose a new model
that is a combination of several simple ones. It is more accurate, requires only minor
additional computations and thus is well suited for use in mobile applications.
%
The to-be-expected accuracy (in \decibel{} and \meter{}) of all models is analyzed for various setups ranging from
just empirical parameters (no setup time when transmitter positions are known) to optimized
parameters, where no prior knowledge is necessary and a few reference measurements suffice.
Despite analyzing the \docWIFI{} performance on its own, we will also have
a closer look at the to-be-expected performance within a complete indoor
localization setup using a floorplan-based movement model together with
various sensors via recursive state estimation based on a particle filter.
a closer look at the resulting performance-changes within a fully featured smartphone-based
indoor localization system using a movement model based on the building's floorplan,
together with various other sensors and recursive state estimation based on a particle filter.
\todo{
fokus:\\
- wlan parameter + optimierung\\
- evaluation der einzel und gesamtergebnisse
}
%\todo{
%fokus:\\
%- wlan parameter + optimierung\\
%- evaluation der einzel und gesamtergebnisse
%}
\todo{
contribution?:\\
- neues wifi modell,\\
- neues resampling,\\
- model param optimierung + eval was es bringt
}
%\todo{
%contribution?:\\
%- neues wifi modell,\\
%- neues resampling,\\
%- model param optimierung + eval was es bringt
%}

81
tex/chapters/relatedwork.tex
View File

@@ -1,51 +1,58 @@
\section{Related Work}
Indoor localization based on \docWIFI{} signal strengths dates back to the year
2000 and the work of Bahl and Padmanabhan \cite{radar}. During an offline-phase, a
multitude of reference measurements are conducted once. Those measurements are compared
against live readings during an online-phase. The pedestrian's location is inferred
using the $k$-nearest neighbor(s) based on the Euclidean distance between currently
received signal strengths and the readings during the offline phase.
Indoor localization based on received \docWIFI{} signal strengths (RSSI) dates back to the year
2000 and the work of Bahl and Padmanabhan \cite{radar}. During an one-time offline-phase, a
multitude of reference measurements are conducted. During the online-phase, where the pedestrian
walks along the building, those prior measurements are compared against live readings.
The pedestrian's location is inferred using the $k$-nearest neighbor(s) based on the Euclidean distance between currently
received signal strengths and the readings during the offline-phase.
Inspired by this initial work, Youssef et al. \cite{horus} proposed a more robust, probabilistic
approach. Fingerprints were placed every \SI{1.52}{\meter} and estimated by scanning each location
100 times. The resulting signal strength propagation for one location is hereafter denoted by a histogram.
approach. Their fingerprints were placed every \SI{1.52}{\meter} and estimated by scanning each location
100 times. The resulting signal strength distribution for each location is hereafter encoded by a histogram.
The latter can be compared against live measurements to infer its matching-probability. The center
of mass among the $k$ highest probabilities, including their weight, describes the pedestrian's current location.
%
In \cite{ProbabilisticWlan}, a similar approach is used and compared against nearest neighbor and machine learning.
Furthermore, they mention potential issues of unseen transmitters and describe a simple heuristic of how to handle such cases.
In \cite{ProbabilisticWlan}, a similar approach is used and compared against nearest neighbor, kernel-density-estimation and machine learning.
Furthermore, they mention potential issues of (temporarily) invisible transmitters and describe a simple heuristic of how to handle such cases.
Meng et al \cite{secureAndRobust} further discuss several fingerprinting issues like environmental changes
after the fingerprints were recorded. They propose an outlier detected based on RANSAC to remove potentially
Meng et al. \cite{secureAndRobust} further discuss several fingerprinting issues like environmental changes
after the fingerprints were recorded. They propose an outlier detection based on RANSAC to remove potentially
distorted measurements and thus improve the matching process.
Despite a very high accuracy due to real-world comparisons, all approaches suffer from tremendous setup-
and maintainance times.
Despite a very high accuracy due to real-world comparisons, aforementioned approaches suffer from tremendous setup-
and maintainance times.
Using robots instead of human workforce to accurately gather the necessary
fingerprints might thus be a viable choice \cite{robotFingerprinting}.
Being cheaper and more accurate, this technique can also
be combined with SLAM for cases where the floorplan is unavailable.
Therefore it makes sense to replace those time consuming fingerprints by model predictions.
Those are a well established research field, mainly used to determine the \docWIFI{}-coverage
for new installations. \cite{ANewPathLossPrediction, PredictingRFCoverage, empiricalPathLossModel}
Besides using real world measurements via fingerprinting, model predictions can be used to determine
signal strengths for arbitrary locations. Propagation models are a well established field of research,
initially used to determine the \docWIFI{}-coverage for new installations.
While many of them are intended for outdoor and line-of-sight purposes, they are often applied to indoor use-cases as well
\cite{ANewPathLossPrediction, PredictingRFCoverage, empiricalPathLossModel}.
The model-based approach presented by Chintalapudi et al. \cite{WithoutThePain} works without any prior knowledge.
During a setup phase, pedestrians just walk within the building and transmit all observations to a central
server. Some GPS fixes with well known position (e.g. entering and leaving the building) observed by the pedestrians
are used as reference points. A genetic optimization algorithm hereafter estimates both, the parameters for a
signal strength prediction model and the pedestrian's locations during the walk. The estimated parameters
can be refined using additional walks and may hereafter be used for the indoor localization process.
Likewise, it is possible to apply a global optimization that also determines a vague floorplan for the building \cite{crowdinside}.
einfach messen, ab und zu einen GPS fix und danach genetisch alles zuusammenoptimieren. also kein vorwissen
\cite{WithoutThePain}
As described in previous works, signal strength propagation strongly depends on the transmitter's surroundings and thus on the buildings
architecture.
%This induces both, the need for more complex prediction models and the need for filtering approaches
%to limit the impact of potentially erroneous readings.
%
Approaches based on timing like TOA and TDOA as used within the GPS or methods estimating the signal's angle-of-arrival (AOA)
are more accurate, and mostly invariant to architectural obstacles \cite{TimeDifferenceOfArrival1, TOAAOA}.
However, each of those requires special hardware to work.
%
We therefore focus on the well-known RSSI that is available on each commodity smartphone and use a
a simple signal strength prediction model to estimate the most probable location given the phone's observations.
To reduce the prediction error, we propose a new model based on multiple simple ones.
Several strategies to optimize such a model and the to-be-expected accuracy are hereafter discussed and evaluated.
das muesste noch was aehnliches sein:
\cite{crowdinside}
neben signalstärke gibt es noch viele andere methoden über laufzeiten wie beim gps etc.
diese erfordern meist aber spezial-hardware und laufen deshalb nicht so einfach auf dem smartphone [= ueberleitung!]
\cite{secureAndRobust}
andere methoden neben signalstärke
\cite{TimeDifferenceOfArrival1} \cite{TOAAOA}
\cite{Ebner-15}

10
tex/chapters/system.tex
View File

@@ -5,13 +5,13 @@
using recursive density estimation seen in \refeq{eq:recursiveDensity}.
\begin{equation}
\arraycolsep=1.2pt
\begin{array}{ll}
&p(\mStateVec_{t} \mid \mObsVec_{1:t}) \propto\\
&\underbrace{p(\mObsVec_{t} \mid \mStateVec_{t})}_{\text{evaluation}}
%\arraycolsep=1.2pt
%\begin{array}{ll}
p(\mStateVec_{t} \mid \mObsVec_{1:t}) \propto\\
\underbrace{p(\mObsVec_{t} \mid \mStateVec_{t})}_{\text{evaluation}}
\int \underbrace{p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t-1})}_{\text{transition}}
\underbrace{p(\mStateVec_{t-1} \mid \mObsVec_{1:t-1})d\vec{q}_{t-1}}_{\text{recursion}} \enspace,
\end{array}
%\end{array}
\label{eq:recursiveDensity}
\end{equation}

6
tex/chapters/work.tex
View File

@@ -42,7 +42,8 @@
\label{eq:logDistModel}
\end{equation}
The log distance model \cite{TODO} in \refeq{eq:logDistModel} is a commonly used signal strength prediction model that
The log distance model \cite{IntroductionToRadio, WirelessCommunications} in \refeq{eq:logDistModel} is a commonly
used signal strength prediction model that
is intended for line-of-sight predictions. However, depending on the surroundings, the model is versatile enough
to also serve for indoor purposes.
%
@@ -56,7 +57,8 @@
As \mPLE{} depends on the architecture around the transmitter, the model is bound to homogenous surroundings
like one floor, solely divided by drywalls of the same thickness and material.
%
The log normal shadowing model is a slight modification, to adapt the log distance model to indoor use cases.
The log normal shadowing-, or wall-attenuation-factor model \cite{PathLossPredictionModelsForIndoor}
is a slight modification, to adapt the log distance model to indoor use cases.
It introduces an additional parameter, that considers obstacles between (line-of-sight) the \docAPshort{} and the
location in question by attenuating the signal with a constant value.
%