From 67a9f02d6dd293340f8d548aca5def23f31588ae Mon Sep 17 00:00:00 2001 From: kazu Date: Fri, 21 Apr 2017 10:02:22 +0200 Subject: [PATCH] introduction --- tex/chapters/conclusion.tex | 33 ++++++----- tex/chapters/introduction.tex | 108 ++++++++++++++++++++++++---------- tex/chapters/relatedwork.tex | 2 + 3 files changed, 99 insertions(+), 44 deletions(-) diff --git a/tex/chapters/conclusion.tex b/tex/chapters/conclusion.tex index ffd345e..8a4aa4d 100644 --- a/tex/chapters/conclusion.tex +++ b/tex/chapters/conclusion.tex @@ -1,17 +1,22 @@ -conclusion +\section{Conclusion} -beide ansaetze sind in unserem szenario/gebaeude OK: -bekannte AP-pos + empirische parameter -komplette optimierung über fingerprints + beide ansaetze sind in unserem szenario/gebaeude OK: + bekannte AP-pos + empirische parameter + komplette optimierung über fingerprints -100 prozent optimierung ist nicht moeglich, es gibt -immer stellen, die, zugunsten von anderen, schlechter werden. -es haengt auch stark davon ab, was man optimiert, das modell, -die uebereinstimmung, welche fingerprints [schlechte vs. gute stellen] + 100 prozent optimierung ist nicht moeglich, es gibt + immer stellen, die, zugunsten von anderen, schlechter werden. + es haengt auch stark davon ab, was man optimiert, das modell, + die uebereinstimmung, welche fingerprints [schlechte vs. gute stellen] -zudem ist das modell fuer unser gebaeude nicht gut ggeeignet. -zu viele verschiedene materialien und trennwaende, APs immer in raeumen, -nie auf dem flur. viele hindernisse, wenige freie raeume. -andere modelle koennten hier helfen, erfordern dann aber zur -laufzeit mehr berechnung, oder muessten vorab auf einem grid berechnet -werden \todo{cite auf competition} + zudem ist das modell fuer unser gebaeude nicht gut ggeeignet. + zu viele verschiedene materialien und trennwaende, APs immer in raeumen, + nie auf dem flur. viele hindernisse, wenige freie raeume. + andere modelle koennten hier helfen, erfordern dann aber zur + laufzeit mehr berechnung, oder muessten vorab auf einem grid berechnet + werden \todo{cite auf competition} + +\section{Future Work} + + Komplexere Modelle die vorab berechnet werden und dann einfach in einer + Datenstruktur abgelegt sind, die z.B. interpolation erlaubt etc. diff --git a/tex/chapters/introduction.tex b/tex/chapters/introduction.tex index 2dff1ac..d345a37 100644 --- a/tex/chapters/introduction.tex +++ b/tex/chapters/introduction.tex @@ -1,34 +1,82 @@ -introduction +\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 + based on a variety of sensor observations. + % + Among those, the internal IMU, namely accelerometer and gyroscope, is often + used as a core component, that provides accurate relative movement information + like step- and turn-detection. However, this requires the pedestrian's + initial position to be well known, e.g. using a GPS-fix just before + entering the building. Additionally, the sensor's error will sum up over + time. -setupzeiten von indoor systemen sind hoch [fingerprinting] -auch re-calibration kostet oft zeit + Depending on the used sensor 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 + \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, + it can be stabilized by the IMU and vice versa. + + + The downside of such an approach: both sensors 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 + on the surroundings, those values can change rapidly within meters. + % + That is why fingerprinting became popular: 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 + 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 + place. + + To prevent setup- and maintenance effort, models can be used to predict + the signal strengths one should receive at some arbitrary location. + Depending on the used model, only a few parameters and the location of the + transmitter within the building are required. For newer installations + the latter is often available and tagged within the building's floorplan. + %As signals are attenuated by the buildings architecture like walls and floors, + %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. + + 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. + + 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. + + 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. -meistens hat man einen gebäudeplan -oft auch die info wo APs hängen -warum das nicht nutzen und mit einer groben AP position -+ fixen, empirischen param starten? + \todo{ + fokus:\\ + - wlan parameter + optimierung\\ + - evaluation der einzel und gesamtergebnisse + } -was bekomme ich für eine genauigkeit raus? - -was kann ich machen um das zu verbessern? -model parameter anlernen? - -wo sind die schwächen? -verschiedene modelle mit unterschiedlichem berechnungsaufwand. - -indoor komplett-system mit IMU, abs-heading, rel-heading, wifi sensor -gebäudeplan, bewegungsmodell - -\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 + } diff --git a/tex/chapters/relatedwork.tex b/tex/chapters/relatedwork.tex index 468af66..9a96764 100644 --- a/tex/chapters/relatedwork.tex +++ b/tex/chapters/relatedwork.tex @@ -1,3 +1,5 @@ relatedwork +wifi anfänge von radar (microsoft) etc + \cite{Ebner-15}